From 142b3f46c5f95f21f0f6b7da445707d78814176f Mon Sep 17 00:00:00 2001 From: xiaoiver Date: Thu, 8 Jun 2023 16:17:09 +0800 Subject: [PATCH 01/15] =?UTF-8?q?=E9=A1=B9=E7=9B=AE=E7=BB=93=E6=9E=84?= =?UTF-8?q?=E8=B0=83=E6=95=B4=20&=20=E5=B7=A5=E7=A8=8B=E6=94=B9=E9=80=A0?= =?UTF-8?q?=20(#63)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * chore: use vite instead of father & remove lerna * feat: add wasm graph algorithm * chore: rename @antv/webgpu-graph --- .gitignore | 2 + lerna.json | 7 - package.json | 23 +- .../{webgpu-graph => graph-gpu}/.babelrc.js | 0 .../.prettierrc.js | 0 .../README-zh_CN.md | 2 +- .../{webgpu-graph => graph-gpu}/README.md | 4 +- .../{webgpu-graph => graph-gpu}/package.json | 27 +- .../src/WebGPUGraph.ts | 16 +- .../{webgpu-graph => graph-gpu}/src/index.ts | 0 .../src/link-analysis/index.ts | 0 .../src/link-analysis/pageRank.ts | 42 +- packages/graph-gpu/src/traversal/bfs.ts | 10 + .../src/traversal/index.ts | 0 .../src/traversal/sssp.ts | 44 +- packages/graph-gpu/src/types.ts | 19 + .../{webgpu-graph => graph-gpu}/src/util.ts | 13 +- packages/graph-gpu/tsconfig.json | 23 + .../webpack.config.js | 17 +- .../webpack.dev.config.js | 0 packages/graph-rust/Cargo.toml | 23 + packages/graph-wasm/.gitignore | 5 + packages/graph-wasm/Cargo.lock | 827 + packages/graph-wasm/Cargo.toml | 38 + packages/graph-wasm/README.md | 0 packages/graph-wasm/package.json | 51 + packages/graph-wasm/rust-src/lib.rs | 56 + packages/graph-wasm/rustfmt.toml | 8 + packages/graph-wasm/src/index.ts | 4 + packages/graph-wasm/src/interface.ts | 3 + packages/graph-wasm/src/main.ts | 28 + packages/graph-wasm/src/wasm-worker.js | 45 + packages/graph-wasm/webpack.config.js | 23 + packages/graph/.fatherrc.js | 5 - packages/graph/package-lock.json | 398 - packages/graph/package.json | 18 +- packages/graph/tsconfig.json | 22 +- packages/webgpu-graph/.fatherrc.js | 5 - packages/webgpu-graph/src/traversal/bfs.ts | 14 - packages/webgpu-graph/src/types.ts | 27 - packages/webgpu-graph/tsconfig.json | 27 - packages/webgpu-graph/yarn.lock | 23604 ---------------- pnpm-workspace.yaml | 2 + site/benchmark/index.html | 187 + site/benchmark/main.ts | 111 + site/benchmark/page-rank.ts | 48 + site/benchmark/sssp.ts | 16 + site/datasets.ts | 169 + site/index.html | 19 + site/types.ts | 6 + vite.config.js | 19 + 51 files changed, 1859 insertions(+), 24198 deletions(-) delete mode 100644 lerna.json rename packages/{webgpu-graph => graph-gpu}/.babelrc.js (100%) rename packages/{webgpu-graph => graph-gpu}/.prettierrc.js (100%) rename packages/{webgpu-graph => graph-gpu}/README-zh_CN.md (96%) rename packages/{webgpu-graph => graph-gpu}/README.md (79%) rename packages/{webgpu-graph => graph-gpu}/package.json (82%) rename packages/{webgpu-graph => graph-gpu}/src/WebGPUGraph.ts (69%) rename packages/{webgpu-graph => graph-gpu}/src/index.ts (100%) rename packages/{webgpu-graph => graph-gpu}/src/link-analysis/index.ts (100%) rename packages/{webgpu-graph => graph-gpu}/src/link-analysis/pageRank.ts (81%) create mode 100644 packages/graph-gpu/src/traversal/bfs.ts rename packages/{webgpu-graph => graph-gpu}/src/traversal/index.ts (100%) rename packages/{webgpu-graph => graph-gpu}/src/traversal/sssp.ts (85%) create mode 100644 packages/graph-gpu/src/types.ts rename packages/{webgpu-graph => graph-gpu}/src/util.ts (73%) create mode 100644 packages/graph-gpu/tsconfig.json rename packages/{webgpu-graph => graph-gpu}/webpack.config.js (78%) rename packages/{webgpu-graph => graph-gpu}/webpack.dev.config.js (100%) create mode 100644 packages/graph-rust/Cargo.toml create mode 100644 packages/graph-wasm/.gitignore create mode 100644 packages/graph-wasm/Cargo.lock create mode 100644 packages/graph-wasm/Cargo.toml create mode 100644 packages/graph-wasm/README.md create mode 100644 packages/graph-wasm/package.json create mode 100644 packages/graph-wasm/rust-src/lib.rs create mode 100644 packages/graph-wasm/rustfmt.toml create mode 100644 packages/graph-wasm/src/index.ts create mode 100644 packages/graph-wasm/src/interface.ts create mode 100644 packages/graph-wasm/src/main.ts create mode 100644 packages/graph-wasm/src/wasm-worker.js create mode 100644 packages/graph-wasm/webpack.config.js delete mode 100644 packages/graph/.fatherrc.js delete mode 100644 packages/graph/package-lock.json delete mode 100644 packages/webgpu-graph/.fatherrc.js delete mode 100644 packages/webgpu-graph/src/traversal/bfs.ts delete mode 100644 packages/webgpu-graph/src/types.ts delete mode 100644 packages/webgpu-graph/tsconfig.json delete mode 100644 packages/webgpu-graph/yarn.lock create mode 100644 pnpm-workspace.yaml create mode 100644 site/benchmark/index.html create mode 100644 site/benchmark/main.ts create mode 100644 site/benchmark/page-rank.ts create mode 100644 site/benchmark/sssp.ts create mode 100644 site/datasets.ts create mode 100644 site/index.html create mode 100644 site/types.ts create mode 100644 vite.config.js diff --git a/.gitignore b/.gitignore index 867315c..04afe08 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,5 @@ es dist *.pem !mock-cert.pem +pnpm-lock.yaml +tsconfig.tsbuildinfo diff --git a/lerna.json b/lerna.json deleted file mode 100644 index 27e34c4..0000000 --- a/lerna.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "packages": [ - "packages/*" - ], - "version": "*", - "npmClient": "tnpm" -} diff --git a/package.json b/package.json index 7eb944a..0708270 100644 --- a/package.json +++ b/package.json @@ -2,18 +2,25 @@ "name": "@antv/algorithm", "private": true, "scripts": { - "build": "lerna build", - "lint": "lerna run lint", - "test": "lerna run test --no-private", + "dev": "vite", + "build": "pnpm -r run build", + "lint": "pnpm -r run lint", + "test": "pnpm -r run test --no-private", "prettier": "prettier --write '**/*.{js,jsx,tsx,ts,less,md,json}'", "pretty-quick": "pretty-quick", - "clean": "lerna clean", - "clear": "lerna clean && lerna clean -y", + "clean": "pnpm -r run clean", "clean:modules": "rimraf node_modules", - "bootstrap": "lerna bootstrap", - "ls": "lerna list" + "ls": "pnpm -r list", + "build:site": "vite build", + "deploy": "gh-pages -d site/dist" }, "devDependencies": { - "lerna": "^3.20.2" + "@antv/graphlib": "^2.0.0", + "graphology": "^0.25.1", + "graphology-generators": "^0.11.2", + "graphology-types": "^0.24.7", + "graphology-metrics": "^2.1.0", + "graphology-shortest-path": "^2.0.2", + "vite": "^4.3.8" } } diff --git a/packages/webgpu-graph/.babelrc.js b/packages/graph-gpu/.babelrc.js similarity index 100% rename from packages/webgpu-graph/.babelrc.js rename to packages/graph-gpu/.babelrc.js diff --git a/packages/webgpu-graph/.prettierrc.js b/packages/graph-gpu/.prettierrc.js similarity index 100% rename from packages/webgpu-graph/.prettierrc.js rename to packages/graph-gpu/.prettierrc.js diff --git a/packages/webgpu-graph/README-zh_CN.md b/packages/graph-gpu/README-zh_CN.md similarity index 96% rename from packages/webgpu-graph/README-zh_CN.md rename to packages/graph-gpu/README-zh_CN.md index f95ce98..556d8e3 100644 --- a/packages/webgpu-graph/README-zh_CN.md +++ b/packages/graph-gpu/README-zh_CN.md @@ -28,7 +28,7 @@ document.head.appendChild(tokenElement); 均为异步调用，以 pageRank 为例： ```js -import { pageRank, WebGPUGraph } from '@antv/webgpu-graph'; +import { pageRank, WebGPUGraph } from '@antv/graph-gpu'; // 初始化 const graph = new WebGPUGraph(); diff --git a/packages/webgpu-graph/README.md b/packages/graph-gpu/README.md similarity index 79% rename from packages/webgpu-graph/README.md rename to packages/graph-gpu/README.md index d9fb042..7081554 100644 --- a/packages/webgpu-graph/README.md +++ b/packages/graph-gpu/README.md @@ -1,6 +1,6 @@ # AntV Graph Algorithm based on WebGPU -`webgpu-graph` is a GPU accelerated graph analytics library, with functionality like [WebGPU](https://www.w3.org/TR/webgpu/) which provides modern features such as compute shader(in [WGSL](https://www.w3.org/TR/WGSL/)). Compared with CPU version, we almost gain ~100x speed up with big datasets. +`graph-gpu` is a GPU accelerated graph analytics library, with functionality like [WebGPU](https://www.w3.org/TR/webgpu/) which provides modern features such as compute shader(in [WGSL](https://www.w3.org/TR/WGSL/)). Compared with CPU version, we almost gain ~100x speed up with big datasets. It's inspired by [cuGraph](https://github.com/rapidsai/cugraph) and other implementations based on CUDA. @@ -22,7 +22,7 @@ Since we are using latest syntax of WGSL, you'd better update your Chrome to the ## Usage ```js -import { pageRank, WebGPUGraph } from '@antv/webgpu-graph'; +import { pageRank, WebGPUGraph } from '@antv/graph-gpu'; // initialize WebGPU context const graph = new WebGPUGraph(); diff --git a/packages/webgpu-graph/package.json b/packages/graph-gpu/package.json similarity index 82% rename from packages/webgpu-graph/package.json rename to packages/graph-gpu/package.json index 3f041e3..8eb5c94 100644 --- a/packages/webgpu-graph/package.json +++ b/packages/graph-gpu/package.json @@ -1,5 +1,5 @@ { - "name": "@antv/webgpu-graph", + "name": "@antv/graph-gpu", "version": "1.0.0", "description": "provide common graph algorithms implemented with WebGPU", "keywords": [ @@ -14,23 +14,23 @@ }, "files": [ "package.json", - "es", "lib", "dist", "LICENSE", "README.md", "README-zh_CN.md" ], - "main": "lib/index.js", - "module": "es/index.js", + "main": "dist/index.min.js", + "module": "lib/index.js", "types": "lib/index.d.ts", "unpkg": "dist/index.min.js", "scripts": { - "build": "npm run clean && father build && npm run build:umd", + "build": "npm run clean && npm run build:esm && npm run build:umd", + "build:esm": "tsc", "build:umd": "webpack --config webpack.config.js --mode production", "dev:umd": "webpack --config webpack.dev.config.js --mode development", "ci": "npm run build && npm run coverage", - "clean": "rimraf es lib dist", + "clean": "rimraf lib dist", "coverage": "jest --coverage", "lint": "eslint --ext .js,.jsx,.ts,.tsx --format=pretty \"./\"", "lint:src": "eslint --ext .ts --format=pretty \"./src\"", @@ -59,23 +59,22 @@ "@types/jest": "^26.0.18", "@umijs/fabric": "^2.5.6", "babel-loader": "^8.2.2", - "father": "^2.30.0", "jest": "^26.6.3", - "jest-electron": "^0.1.11", "rimraf": "^3.0.2", "ts-jest": "^26.4.4", "ts-loader": "^8.0.14", "tslint": "^6.1.3", "typescript": "^4.1.3", - "webpack": "^5.17.0", - "webpack-cli": "^4.9.1" + "webpack": "^5.38.1", + "webpack-cli": "^5.0.2" }, "dependencies": { - "@antv/g": "^5.0.16", - "@antv/g-webgl": "^1.0.19", - "@antv/g-plugin-gpgpu": "^1.0.14", + "@antv/g": "^5.16.26", + "@antv/g-webgpu": "^1.7.67", + "@antv/g-plugin-gpgpu": "^1.7.49", + "@antv/graphlib": "^2.0.0", "@types/offscreencanvas": "^2019.6.4", "@webgpu/types": "^0.1.6", - "tslib": "^2.0.0" + "tslib": "^2.5.2" } } diff --git a/packages/webgpu-graph/src/WebGPUGraph.ts b/packages/graph-gpu/src/WebGPUGraph.ts similarity index 69% rename from packages/webgpu-graph/src/WebGPUGraph.ts rename to packages/graph-gpu/src/WebGPUGraph.ts index c196cd1..a75208f 100644 --- a/packages/webgpu-graph/src/WebGPUGraph.ts +++ b/packages/graph-gpu/src/WebGPUGraph.ts @@ -1,9 +1,9 @@ import { Canvas } from '@antv/g'; -import { Renderer } from '@antv/g-webgl'; +import { Renderer } from '@antv/g-webgpu'; import { Plugin } from '@antv/g-plugin-gpgpu'; import { pageRank } from './link-analysis'; import { sssp } from './traversal'; -import type { GraphData } from './types'; +import { Graph } from './types'; export interface WebGPUGraphOptions { canvas: HTMLCanvasElement | OffscreenCanvas; @@ -17,14 +17,15 @@ export class WebGPUGraph { const { canvas } = options; // FIXME: use OffscreenCanvas instead of a real DOM - const $canvas = canvas || window.document.createElement('canvas'); + const $canvas = (canvas || window.document.createElement('canvas')) as HTMLCanvasElement; // use WebGPU - this.renderer = new Renderer({ targets: ['webgpu'] }); + this.renderer = new Renderer(); this.renderer.registerPlugin(new Plugin()); // create a canvas this.canvas = new Canvas({ + // @ts-ignore canvas: $canvas, width: 1, height: 1, @@ -36,15 +37,16 @@ export class WebGPUGraph { // wait for canvas' services ready await this.canvas.ready; // get GPU Device - return this.renderer.getDevice(); + const plugin = this.renderer.getPlugin('device-renderer') as any; + return plugin.getDevice(); } - async pageRank(graphData: GraphData, eps = 1e-5, alpha = 0.85, maxIteration = 1000) { + async pageRank(graphData: Graph, eps = 1e-5, alpha = 0.85, maxIteration = 1000) { const device = await this.getDevice(); return pageRank(device, graphData, eps, alpha, maxIteration); } - async sssp(graphData: GraphData, sourceId: string, weightPropertyName: string = '') { + async sssp(graphData: Graph, sourceId: string, weightPropertyName: string = 'weight') { const device = await this.getDevice(); return sssp(device, graphData, sourceId, weightPropertyName); } diff --git a/packages/webgpu-graph/src/index.ts b/packages/graph-gpu/src/index.ts similarity index 100% rename from packages/webgpu-graph/src/index.ts rename to packages/graph-gpu/src/index.ts diff --git a/packages/webgpu-graph/src/link-analysis/index.ts b/packages/graph-gpu/src/link-analysis/index.ts similarity index 100% rename from packages/webgpu-graph/src/link-analysis/index.ts rename to packages/graph-gpu/src/link-analysis/index.ts diff --git a/packages/webgpu-graph/src/link-analysis/pageRank.ts b/packages/graph-gpu/src/link-analysis/pageRank.ts similarity index 81% rename from packages/webgpu-graph/src/link-analysis/pageRank.ts rename to packages/graph-gpu/src/link-analysis/pageRank.ts index 8b2d6a8..ba2eeb5 100644 --- a/packages/webgpu-graph/src/link-analysis/pageRank.ts +++ b/packages/graph-gpu/src/link-analysis/pageRank.ts @@ -1,19 +1,27 @@ -import type { WebGLRenderer } from '@antv/g-webgl'; -import { Kernel, BufferUsage } from '@antv/g-plugin-gpgpu'; -import type { GraphData } from '../types'; +import { DeviceRenderer } from '@antv/g-webgpu'; +import { Kernel } from '@antv/g-plugin-gpgpu'; import { convertGraphData2CSC } from '../util'; +import { Graph } from '../types'; + +const { BufferUsage } = DeviceRenderer; /** * Pagerank using power method, ported from CUDA - * + * * @param graphData * @param eps Set the tolerance the approximation, this parameter should be a small magnitude value. The lower the tolerance the better the approximation. * @param alpha The damping factor alpha represents the probability to follow an outgoing edge, standard value is 0.85. * @param maxIteration Set the maximum number of iterations. - * + * * @see https://github.com/princeofpython/PageRank-with-CUDA/blob/main/parallel.cu */ -export async function pageRank(device: WebGLRenderer.Device, graphData: GraphData, eps = 1e-05, alpha = 0.85, maxIteration = 1000) { +export async function pageRank( + device: DeviceRenderer.Device, + graphData: Graph, + eps = 1e-5, + alpha = 0.85, + maxIteration = 1000, +) { const BLOCK_SIZE = 1; const BLOCKS = 256; @@ -46,13 +54,13 @@ export async function pageRank(device: WebGLRenderer.Device, graphData: GraphDat const storeKernel = new Kernel(device, { computeShader: ` struct Buffer { - data: array; + data: array, }; @group(0) @binding(0) var r : Buffer; -@group(0) @binding(1) var r_last : Buffer; +@group(0) @binding(1) var r_last : Buffer; -@stage(compute) @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) +@compute @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) fn main( @builtin(global_invocation_id) global_id : vec3 ) { @@ -66,14 +74,14 @@ fn main( const matmulKernel = new Kernel(device, { computeShader: ` struct Buffer { - data: array; + data: array, }; @group(0) @binding(0) var graph : Buffer; @group(0) @binding(1) var r : Buffer; @group(0) @binding(2) var r_last : Buffer; -@stage(compute) @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) +@compute @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) fn main( @builtin(global_invocation_id) global_id : vec3 ) { @@ -92,13 +100,13 @@ fn main( const rankDiffKernel = new Kernel(device, { computeShader: ` struct Buffer { - data: array; + data: array, }; @group(0) @binding(0) var r : Buffer; @group(0) @binding(1) var r_last : Buffer; -@stage(compute) @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) +@compute @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) fn main( @builtin(global_invocation_id) global_id : vec3 ) { @@ -142,15 +150,15 @@ fn main( matmulKernel.dispatch(grids, 1); rankDiffKernel.dispatch(grids, 1); - const last = await readback.readBuffer(rLastBuffer) as Float32Array; + const last = (await readback.readBuffer(rLastBuffer)) as Float32Array; const result = last.reduce((prev, cur) => prev + cur, 0); if (result < eps) { break; } } - const out = await readback.readBuffer(rBuffer) as Float32Array; + const out = (await readback.readBuffer(rBuffer)) as Float32Array; return Array.from(out) - .map((score, index) => ({ id: graphData.nodes[index].id, score })) + .map((score, index) => ({ id: graphData.getAllNodes()[index].id, score })) .sort((a, b) => b.score - a.score); -} \ No newline at end of file +} diff --git a/packages/graph-gpu/src/traversal/bfs.ts b/packages/graph-gpu/src/traversal/bfs.ts new file mode 100644 index 0000000..b347b68 --- /dev/null +++ b/packages/graph-gpu/src/traversal/bfs.ts @@ -0,0 +1,10 @@ +import { DeviceRenderer } from '@antv/g-webgpu'; +import { Graph } from '../types'; + +/** + * Scalable GPU Graph Traversal + * @see https://research.nvidia.com/publication/scalable-gpu-graph-traversal + * @see https://github.com/rafalk342/bfs-cuda + * @see https://github.com/kaletap/bfs-cuda-gpu + */ +export async function bfs(device: DeviceRenderer.Device, graphData: Graph) {} diff --git a/packages/webgpu-graph/src/traversal/index.ts b/packages/graph-gpu/src/traversal/index.ts similarity index 100% rename from packages/webgpu-graph/src/traversal/index.ts rename to packages/graph-gpu/src/traversal/index.ts diff --git a/packages/webgpu-graph/src/traversal/sssp.ts b/packages/graph-gpu/src/traversal/sssp.ts similarity index 85% rename from packages/webgpu-graph/src/traversal/sssp.ts rename to packages/graph-gpu/src/traversal/sssp.ts index d23a80f..82638ae 100644 --- a/packages/webgpu-graph/src/traversal/sssp.ts +++ b/packages/graph-gpu/src/traversal/sssp.ts @@ -1,18 +1,26 @@ -import type { WebGLRenderer } from '@antv/g-webgl'; -import { Kernel, BufferUsage } from '@antv/g-plugin-gpgpu'; -import { GraphData } from '../types'; +import { DeviceRenderer } from '@antv/g-webgpu'; +import { Kernel } from '@antv/g-plugin-gpgpu'; +import { Graph } from '../types'; import { convertGraphData2CSC } from '../util'; +const { BufferUsage } = DeviceRenderer; + /** * SSSP(Bellman-Ford) ported from CUDA - * + * * @see https://www.lewuathe.com/illustration-of-distributed-bellman-ford-algorithm.html * @see https://github.com/sengorajkumar/gpu_graph_algorithms * @see https://docs.rapids.ai/api/cugraph/stable/api_docs/api/cugraph.traversal.sssp.sssp.html * compared with G6: * @see https://g6.antv.vision/zh/docs/api/Algorithm#findshortestpathgraphdata-start-end-directed-weightpropertyname */ -export async function sssp(device: WebGLRenderer.Device, graphData: GraphData, sourceId: string, weightPropertyName: string = '', maxDistance = 1000000) { +export async function sssp( + device: DeviceRenderer.Device, + graphData: Graph, + sourceId: string, + weightPropertyName: string = '', + maxDistance = 1000000, +) { // The total number of workgroup invocations (4096) exceeds the maximum allowed (256). const BLOCK_SIZE = 1; const BLOCKS = 256; @@ -22,7 +30,7 @@ export async function sssp(device: WebGLRenderer.Device, graphData: GraphData, s let W: number[]; const sourceIdx = nodeId2IndexMap[sourceId]; if (weightPropertyName) { - W = edges.map((edgeConfig) => Number(edgeConfig[weightPropertyName])); + W = edges.map(edgeConfig => Number(edgeConfig.data[weightPropertyName])); } else { // all the vertex has the same weight W = new Array(E.length).fill(1); @@ -31,10 +39,10 @@ export async function sssp(device: WebGLRenderer.Device, graphData: GraphData, s const relaxKernel = new Kernel(device, { computeShader: ` struct Buffer { - data: array; + data: array, }; struct AtomicBuffer { - data: array>; + data: array>, }; @group(0) @binding(0) var d_in_E : Buffer; @@ -43,7 +51,7 @@ struct AtomicBuffer { @group(0) @binding(3) var d_out_D : Buffer; @group(0) @binding(4) var d_out_Di : AtomicBuffer; -@stage(compute) @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) +@compute @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) fn main( @builtin(global_invocation_id) global_id : vec3 ) { @@ -69,13 +77,13 @@ fn main( const updateDistanceKernel = new Kernel(device, { computeShader: ` struct Buffer { - data: array; + data: array, }; @group(0) @binding(0) var d_out_D : Buffer; @group(0) @binding(1) var d_out_Di : Buffer; -@stage(compute) @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) +@compute @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) fn main( @builtin(global_invocation_id) global_id : vec3 ) { @@ -93,10 +101,10 @@ fn main( const updatePredKernel = new Kernel(device, { computeShader: ` struct Buffer { - data: array; + data: array, }; struct AtomicBuffer { - data: array>; + data: array>, }; @group(0) @binding(0) var d_in_V : Buffer; @@ -106,7 +114,7 @@ struct AtomicBuffer { @group(0) @binding(4) var d_out_D : Buffer; @group(0) @binding(5) var d_out_P : AtomicBuffer; -@stage(compute) @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) +@compute @workgroup_size(${BLOCKS}, ${BLOCK_SIZE}) fn main( @builtin(global_invocation_id) global_id : vec3 ) { @@ -187,12 +195,12 @@ fn main( } updatePredKernel.dispatch(grids, 1); - const out = await readback.readBuffer(DiOutBuffer) as Float32Array; + const out = (await readback.readBuffer(DiOutBuffer)) as Float32Array; const predecessor = await readback.readBuffer(POutBuffer); return Array.from(out).map((distance, i) => ({ - target: graphData.nodes[V[i]].id, + target: graphData.getAllNodes()[V[i]].id, distance, - predecessor: graphData.nodes[V[predecessor[i]]].id, + predecessor: graphData.getAllNodes()[V[predecessor[i]]]?.id, })); -} \ No newline at end of file +} diff --git a/packages/graph-gpu/src/types.ts b/packages/graph-gpu/src/types.ts new file mode 100644 index 0000000..0409dec --- /dev/null +++ b/packages/graph-gpu/src/types.ts @@ -0,0 +1,19 @@ +import { Edge, Graph as IGraph, PlainObject } from '@antv/graphlib'; + +export interface NodeData extends PlainObject {} + +export interface EdgeData extends PlainObject { + weight?: number; +} + +export type Graph = IGraph; + +export interface CSC { + V: number[]; + E: number[]; + I: number[]; + From: number[]; + To: number[]; + nodeId2IndexMap: Record; + edges: Edge[]; +} diff --git a/packages/webgpu-graph/src/util.ts b/packages/graph-gpu/src/util.ts similarity index 73% rename from packages/webgpu-graph/src/util.ts rename to packages/graph-gpu/src/util.ts index ff6c0f3..1e9b475 100644 --- a/packages/webgpu-graph/src/util.ts +++ b/packages/graph-gpu/src/util.ts @@ -1,22 +1,23 @@ -import type { GraphData, CSC, EdgeConfig } from './types'; +import { Edge, ID } from '@antv/graphlib'; +import type { CSC, EdgeData, Graph } from './types'; -export function convertGraphData2CSC(graphData: GraphData): CSC { +export function convertGraphData2CSC(graphData: Graph): CSC { const V: number[] = []; const E: number[] = []; const From: number[] = []; const To: number[] = []; const I: number[] = []; const nodeId2IndexMap: Record = {}; - const edges: EdgeConfig[] = [] - graphData.nodes.forEach((node, i) => { + const edges: Edge[] = [] + graphData.getAllNodes().forEach((node, i) => { nodeId2IndexMap[node.id] = i; V.push(i); }); - let lastSource = ''; + let lastSource: ID = ''; let counter = 0; // sort by source - [...graphData.edges] + [...graphData.getAllEdges()] .sort((a, b) => nodeId2IndexMap[a.source] - nodeId2IndexMap[b.source]) .forEach((edgeConfig) => { const { source, target } = edgeConfig; diff --git a/packages/graph-gpu/tsconfig.json b/packages/graph-gpu/tsconfig.json new file mode 100644 index 0000000..ba439d3 --- /dev/null +++ b/packages/graph-gpu/tsconfig.json @@ -0,0 +1,23 @@ +{ + "compilerOptions": { + "rootDir": "./src", + "composite": true, + "module": "esnext", + "declaration": true, + "sourceMap": true, + "target": "es5", + "outDir": "lib", + "jsx": "react", + "importHelpers": true, + "moduleResolution": "node", + "allowSyntheticDefaultImports": true, + "resolveJsonModule": true, + "esModuleInterop": true, + "lib": ["esnext", "dom"], + "experimentalDecorators": false /* Enables experimental support for ES7 decorators. */, + // "emitDecoratorMetadata": true, /* Enables experimental support for emitting type metadata for decorators. */ + "downlevelIteration": true, + "skipLibCheck": true + }, + "include": ["src/**/*"] +} diff --git a/packages/webgpu-graph/webpack.config.js b/packages/graph-gpu/webpack.config.js similarity index 78% rename from packages/webgpu-graph/webpack.config.js rename to packages/graph-gpu/webpack.config.js index 327b2f8..a85b05a 100644 --- a/packages/webgpu-graph/webpack.config.js +++ b/packages/graph-gpu/webpack.config.js @@ -7,7 +7,7 @@ module.exports = { }, output: { filename: '[name].min.js', - library: 'WebGPUGraph', + library: 'GraphGPU', libraryTarget: 'umd', libraryExport: 'default', path: resolve(process.cwd(), 'dist/'), @@ -15,26 +15,13 @@ module.exports = { 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= ["js"] } +graph = { version = "0.3.0", features = ["serde"] } +console_error_panic_hook = "0.1" +js-sys = "0.3.51" +serde-wasm-bindgen = "0.5" +serde = { version = "1.0", features = ["derive"] } +serde_json = "1.0" +wasm-bindgen = { version = "0.2", features = ["serde-serialize"] } +wasm-bindgen-rayon = { version = "1.0", optional = true } +wee_alloc = { version = "0.4", optional = true } + +[features] +parallel = ["wasm-bindgen-rayon", "wee_alloc"] \ No newline at end of file diff --git a/packages/graph-wasm/README.md b/packages/graph-wasm/README.md new file mode 100644 index 0000000..e69de29 diff --git a/packages/graph-wasm/package.json b/packages/graph-wasm/package.json new file mode 100644 index 0000000..9ff17cd --- /dev/null +++ b/packages/graph-wasm/package.json @@ -0,0 +1,51 @@ +{ + "name": "@antv/graph-wasm", + "version": "0.0.1", + "description": "A WASM binding of rust implementation for graph algorithms.", + "license": "MIT", + "repository": { + "type": "git", + "url": "https://github.com/antvis/algorithm", + "directory": "packages/graph-wasm" + }, + "main": "dist/index.min.js", + "module": "lib/index.js", + "types": "lib/index.d.ts", + "unpkg": "dist/index.min.js", + "files": [ + "package.json", + "dist", + "lib", + "pkg/**/*", + "pkg-parallel/**/*", + "pkg-node/**/*", + "LICENSE", + "README.md" + ], + "sideEffects": false, + "scripts": { + "build:wasm-st": "wasm-pack build --target web", + "build:wasm-mt": "cross-env RUSTFLAGS='-C target-feature=+atomics,+bulk-memory,+mutable-globals' rustup run nightly wasm-pack build --target web --out-dir pkg-parallel -- --features parallel -Z build-std=panic_abort,std", + "build:wasm-node": "wasm-pack build --target nodejs --out-dir pkg-node", + "build:wasm": "run-p build:wasm-st build:wasm-mt build:wasm-node", + "build:esm": "tsc", + "build:umd": "webpack --config webpack.config.js --mode production", + "build": "npm run clean && npm run build:wasm && npm run build:esm && npm run build:umd", + "clean": "rimraf dist pkg pkg-parallel pkg-node" + }, + "dependencies": { + "@antv/util": "^3.3.2", + "comlink": "^4.3.1", + "wasm-feature-detect": "^1.2.10", + "tslib": "^2.5.0" + }, + "devDependencies": { + "cross-env": "^7.0.3", + "npm-run-all": "^4.1.5", + "rimraf": "^3.0.2", + "ts-loader": "^7.0.3", + "typescript": "^4.0.3", + "webpack": "^5.38.1", + "webpack-cli": "^5.0.2" + } +} diff --git a/packages/graph-wasm/rust-src/lib.rs b/packages/graph-wasm/rust-src/lib.rs new file mode 100644 index 0000000..c08f7ae --- /dev/null +++ b/packages/graph-wasm/rust-src/lib.rs @@ -0,0 +1,56 @@ +use graph::prelude::*; +use js_sys::Array; +use wasm_bindgen::prelude::*; + +#[cfg(feature = "parallel")] +pub use wasm_bindgen_rayon::init_thread_pool; + +#[cfg(feature = "wee_alloc")] +#[global_allocator] +static ALLOC: wee_alloc::WeeAlloc = wee_alloc::WeeAlloc::INIT; + +#[wasm_bindgen(start)] +pub fn start() { + console_error_panic_hook::set_once(); +} + +#[wasm_bindgen(js_name = "pageRank")] +pub fn page_rank(val: JsValue) -> Array { + let options: PageRankConfig = serde_wasm_bindgen::from_value(val).unwrap(); + + let graph: DirectedCsrGraph = GraphBuilder::new() + .edges(vec![ + (1, 2), // B->C + (2, 1), // C->B + (4, 0), // D->A + (4, 1), // D->B + (5, 4), // E->D + (5, 1), // E->B + (5, 6), // E->F + (6, 1), // F->B + (6, 5), // F->E + (7, 1), // G->B + (7, 5), // F->E + (8, 1), // G->B + (8, 5), // G->E + (9, 1), // H->B + (9, 5), // H->E + (10, 1), // I->B + (10, 5), // I->E + (11, 5), // J->B + (12, 5), // K->B + ]) + .build(); + + let (ranks, iterations, _) = graph::prelude::page_rank( + &graph, + PageRankConfig::new( + options.max_iterations, + options.tolerance, + options.damping_factor, + ), + ); + + // @see https://stackoverflow.com/a/58996628 + ranks.into_iter().map(JsValue::from).collect() +} diff --git a/packages/graph-wasm/rustfmt.toml b/packages/graph-wasm/rustfmt.toml new file mode 100644 index 0000000..6df7c47 --- /dev/null +++ b/packages/graph-wasm/rustfmt.toml @@ -0,0 +1,8 @@ +hard_tabs = false +newline_style = "unix" + +unstable_features = true +format_code_in_doc_comments = true +format_macro_bodies = true +format_macro_matchers = true +format_strings = true diff --git a/packages/graph-wasm/src/index.ts b/packages/graph-wasm/src/index.ts new file mode 100644 index 0000000..fea5c89 --- /dev/null +++ b/packages/graph-wasm/src/index.ts @@ -0,0 +1,4 @@ +import { supportsThreads, initThreads } from './main'; +import type { Threads } from "./interface"; + +export { supportsThreads, initThreads, Threads }; diff --git a/packages/graph-wasm/src/interface.ts b/packages/graph-wasm/src/interface.ts new file mode 100644 index 0000000..6ce1c7c --- /dev/null +++ b/packages/graph-wasm/src/interface.ts @@ -0,0 +1,3 @@ +export interface Threads { + page_rank: (options: any) => Promise<{ ranks: number[] }>; +} diff --git a/packages/graph-wasm/src/main.ts b/packages/graph-wasm/src/main.ts new file mode 100644 index 0000000..31ab55a --- /dev/null +++ b/packages/graph-wasm/src/main.ts @@ -0,0 +1,28 @@ +import { threads } from "wasm-feature-detect"; +import * as Comlink from "comlink"; +import type { Threads } from "./interface"; + +/** + * [Not all browsers](https://webassembly.org/roadmap/) support WebAssembly threads yet, + * so you'll likely want to make two builds: + * - one with threads support + * - one without + * + * and use feature detection to choose the right one on the JavaScript side. + */ +export async function supportsThreads() { + return threads(); +} + +export async function initThreads(useMultiThread = true): Promise { + const initHandlers = Comlink.wrap( + // @ts-ignore + new Worker(new URL("./wasm-worker.js", import.meta.url), { + type: "module", + }) + ); + + // @ts-ignore + const handlers = await initHandlers(useMultiThread); + return handlers; +} \ No newline at end of file diff --git a/packages/graph-wasm/src/wasm-worker.js b/packages/graph-wasm/src/wasm-worker.js new file mode 100644 index 0000000..ff2fbb2 --- /dev/null +++ b/packages/graph-wasm/src/wasm-worker.js @@ -0,0 +1,45 @@ +import * as Comlink from "comlink"; + +const DEFAULT_PAGE_RANK_OPTIONS = { + max_iterations: 10, + tolerance: 0.0001, + damping_factor: 0.85, +}; + +const wrapTransfer = (page_rank) => { + return (options) => { + const ranks = page_rank({ + ...DEFAULT_PAGE_RANK_OPTIONS, + ...options, + }); + + return { + // Little perf boost to transfer data to the main thread w/o copying. + ranks: Comlink.transfer(ranks, [ranks]), + }; + }; +}; + +// Wrap wasm-bindgen exports (the `generate` function) to add time measurement. +function wrapExports({ page_rank }) { + return { + page_rank: wrapTransfer(page_rank), + }; +} + +async function initHandlers(useMultiThread) { + if (useMultiThread) { + // @ts-ignore + const multiThread = await import("../pkg-parallel/antv_graph_wasm.js"); + await multiThread.default(); + await multiThread.initThreadPool(navigator.hardwareConcurrency); + return Comlink.proxy(wrapExports(multiThread)); + } else { + // @ts-ignore + const singleThread = await import("../pkg/antv_graph_wasm.js"); + await singleThread.default(); + return Comlink.proxy(wrapExports(singleThread)); + } +} + +Comlink.expose(initHandlers); diff --git a/packages/graph-wasm/webpack.config.js b/packages/graph-wasm/webpack.config.js new file mode 100644 index 0000000..7b7a580 --- /dev/null +++ b/packages/graph-wasm/webpack.config.js @@ -0,0 +1,23 @@ +const path = require("path"); + +module.exports = { + entry: "./src/index.ts", + module: { + rules: [ + { + test: /\.tsx?$/, + use: "ts-loader", + exclude: /node_modules/, + }, + ], + }, + resolve: { + extensions: [".tsx", ".ts", ".js"], + }, + output: { + filename: "index.min.js", + path: path.resolve(__dirname, "dist"), + library: "graphWASM", + libraryTarget: "umd", + }, +}; diff --git a/packages/graph/.fatherrc.js b/packages/graph/.fatherrc.js deleted file mode 100644 index 2b646ff..0000000 --- a/packages/graph/.fatherrc.js +++ /dev/null @@ -1,5 +0,0 @@ -export default { - entry: './src/index.ts', - esm: 'babel', - cjs: 'babel' -}; \ No newline at end of file diff --git a/packages/graph/package-lock.json b/packages/graph/package-lock.json deleted file mode 100644 index 6a6094b..0000000 --- a/packages/graph/package-lock.json +++ /dev/null @@ -1,398 +0,0 @@ -{ - "name": "@antv/algorithm", - "version": "0.1.15", - "lockfileVersion": 1, - "requires": true, - "dependencies": { - "@discoveryjs/json-ext": { - "version": "0.5.5", - "resolved": "https://registry.npm.alibaba-inc.com/@discoveryjs/json-ext/download/@discoveryjs/json-ext-0.5.5.tgz", - "integrity": "sha1-koPJzlsomjxPYcEnV0aeWTd/gfM=", - "dev": true - }, - "@webpack-cli/configtest": { - "version": "1.1.0", - "resolved": "https://registry.npm.alibaba-inc.com/@webpack-cli/configtest/download/@webpack-cli/configtest-1.1.0.tgz", - "integrity": "sha1-g0K+8Lrft9/TtXbyV0q4DHJb4EM=", - "dev": true - }, - "@webpack-cli/info": { - "version": "1.4.0", - "resolved": "https://registry.npm.alibaba-inc.com/@webpack-cli/info/download/@webpack-cli/info-1.4.0.tgz", - "integrity": "sha1-uRecMierCcu7FJqnM0dfz5lDAiM=", - "dev": true, - "requires": { - "envinfo": "^7.7.3" - } - }, - "@webpack-cli/serve": { - "version": "1.6.0", - "resolved": "https://registry.npm.alibaba-inc.com/@webpack-cli/serve/download/@webpack-cli/serve-1.6.0.tgz", - "integrity": "sha1-LCdaoFyJXszrv8NM+yI8bovVkaI=", - "dev": true - }, - "clone-deep": { - "version": "4.0.1", - "resolved": "https://registry.npm.alibaba-inc.com/clone-deep/download/clone-deep-4.0.1.tgz", - "integrity": "sha1-wZ/Zvbv4WUK0/ZechNz31fB8I4c=", - "dev": true, - "requires": { - "is-plain-object": "^2.0.4", - "kind-of": "^6.0.2", - "shallow-clone": "^3.0.0" - } - }, - "colorette": { - "version": "2.0.16", - "resolved": "https://registry.npm.alibaba-inc.com/colorette/download/colorette-2.0.16.tgz", - "integrity": "sha1-cTua+E/bAAE58EVGvUqT9ipQhdo=", - "dev": true - }, - "commander": { - "version": "7.2.0", - "resolved": "https://registry.npm.alibaba-inc.com/commander/download/commander-7.2.0.tgz", - "integrity": "sha1-o2y1fQtQHOEI5NIFWaFQo5HZerc=", - "dev": true - }, - "cross-spawn": { - "version": "7.0.3", - "resolved": "https://registry.npm.alibaba-inc.com/cross-spawn/download/cross-spawn-7.0.3.tgz", - "integrity": "sha1-9zqFudXUHQRVUcF34ogtSshXKKY=", - "dev": true, - "requires": { - "path-key": "^3.1.0", - "shebang-command": "^2.0.0", - "which": "^2.0.1" - } - }, - "envinfo": { - "version": "7.8.1", - "resolved": "https://registry.npm.alibaba-inc.com/envinfo/download/envinfo-7.8.1.tgz", - "integrity": "sha1-Bjd+Pl9NN5/qesWS1a2JJ+DE1HU=", - "dev": true - }, - "execa": { - "version": "5.1.1", - "resolved": "https://registry.npm.alibaba-inc.com/execa/download/execa-5.1.1.tgz", - "integrity": "sha1-+ArZy/Qpj3vR1MlVXCHpN0HEEd0=", - "dev": true, - "requires": { - "cross-spawn": "^7.0.3", - "get-stream": "^6.0.0", - "human-signals": "^2.1.0", - "is-stream": "^2.0.0", - "merge-stream": "^2.0.0", - "npm-run-path": "^4.0.1", - "onetime": "^5.1.2", - "signal-exit": "^3.0.3", - "strip-final-newline": "^2.0.0" - } - }, - "fastest-levenshtein": { - "version": "1.0.12", - "resolved": "https://registry.npm.alibaba-inc.com/fastest-levenshtein/download/fastest-levenshtein-1.0.12.tgz", - "integrity": "sha1-mZD306iMxan/0fF0V0UlFwDUl+I=", - "dev": true - }, - "find-up": { - "version": "4.1.0", - "resolved": "https://registry.npm.alibaba-inc.com/find-up/download/find-up-4.1.0.tgz", - "integrity": "sha1-l6/n1s3AvFkoWEt8jXsW6KmqXRk=", - "dev": true, - "requires": { - "locate-path": "^5.0.0", - "path-exists": "^4.0.0" - } - }, - "function-bind": { - "version": "1.1.1", - "resolved": "https://registry.npm.alibaba-inc.com/function-bind/download/function-bind-1.1.1.tgz", - "integrity": "sha1-pWiZ0+o8m6uHS7l3O3xe3pL0iV0=", - "dev": true - }, - "get-stream": { - "version": "6.0.1", - "resolved": "https://registry.npm.alibaba-inc.com/get-stream/download/get-stream-6.0.1.tgz", - "integrity": "sha1-omLY7vZ6ztV8KFKtYWdSakPL97c=", - "dev": true - }, - "has": { - "version": "1.0.3", - "resolved": "https://registry.npm.alibaba-inc.com/has/download/has-1.0.3.tgz", - "integrity": "sha1-ci18v8H2qoJB8W3YFOAR4fQeh5Y=", - "dev": true, - "requires": { - "function-bind": "^1.1.1" - } - }, - "human-signals": { - "version": "2.1.0", - "resolved": "https://registry.npm.alibaba-inc.com/human-signals/download/human-signals-2.1.0.tgz", - "integrity": "sha1-3JH8ukLk0G5Kuu0zs+ejwC9RTqA=", - "dev": true - }, - "import-local": { - "version": "3.0.3", - "resolved": "https://registry.npm.alibaba-inc.com/import-local/download/import-local-3.0.3.tgz", - "integrity": "sha1-TVHCxJXKk5PaJZ7Ga2LgIpICEeA=", - "dev": true, - "requires": { - "pkg-dir": "^4.2.0", - "resolve-cwd": "^3.0.0" - } - }, - "interpret": { - "version": "2.2.0", - "resolved": "https://registry.npm.alibaba-inc.com/interpret/download/interpret-2.2.0.tgz", - "integrity": "sha1-GnigtZZcQKVBbQB61vUK0nxBffk=", - "dev": true - }, - "is-core-module": { - "version": "2.8.0", - "resolved": "https://registry.npm.alibaba-inc.com/is-core-module/download/is-core-module-2.8.0.tgz", - "integrity": "sha1-AyEzbD0JJeSX/Zf12VyxFKXM1Ug=", - "dev": true, - "requires": { - "has": "^1.0.3" - } - }, - "is-plain-object": { - "version": "2.0.4", - "resolved": "https://registry.npm.alibaba-inc.com/is-plain-object/download/is-plain-object-2.0.4.tgz", - "integrity": "sha1-LBY7P6+xtgbZ0Xko8FwqHDjgdnc=", - "dev": true, - "requires": { - "isobject": "^3.0.1" - } - }, - "is-stream": { - "version": "2.0.1", - "resolved": "https://registry.npm.alibaba-inc.com/is-stream/download/is-stream-2.0.1.tgz", - "integrity": "sha1-+sHj1TuXrVqdCunO8jifWBClwHc=", - "dev": true - }, - "isexe": { - "version": "2.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/isexe/download/isexe-2.0.0.tgz", - "integrity": "sha1-6PvzdNxVb/iUehDcsFctYz8s+hA=", - "dev": true - }, - "isobject": { - "version": "3.0.1", - "resolved": "https://registry.npm.alibaba-inc.com/isobject/download/isobject-3.0.1.tgz", - "integrity": "sha1-TkMekrEalzFjaqH5yNHMvP2reN8=", - "dev": true - }, - "kind-of": { - "version": "6.0.3", - "resolved": "https://registry.npm.alibaba-inc.com/kind-of/download/kind-of-6.0.3.tgz", - "integrity": "sha1-B8BQNKbDSfoG4k+jWqdttFgM5N0=", - "dev": true - }, - "locate-path": { - "version": "5.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/locate-path/download/locate-path-5.0.0.tgz", - "integrity": "sha1-Gvujlq/WdqbUJQTQpno6frn2KqA=", - "dev": true, - "requires": { - "p-locate": "^4.1.0" - } - }, - "merge-stream": { - "version": "2.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/merge-stream/download/merge-stream-2.0.0.tgz", - "integrity": "sha1-UoI2KaFN0AyXcPtq1H3GMQ8sH2A=", - "dev": true - }, - "mimic-fn": { - "version": "2.1.0", - "resolved": "https://registry.npm.alibaba-inc.com/mimic-fn/download/mimic-fn-2.1.0.tgz", - "integrity": "sha1-ftLCzMyvhNP/y3pptXcR/CCDQBs=", - "dev": true - }, - "npm-run-path": { - "version": "4.0.1", - "resolved": "https://registry.npm.alibaba-inc.com/npm-run-path/download/npm-run-path-4.0.1.tgz", - "integrity": "sha1-t+zR5e1T2o43pV4cImnguX7XSOo=", - "dev": true, - "requires": { - "path-key": "^3.0.0" - } - }, - "onetime": { - "version": "5.1.2", - "resolved": "https://registry.npm.alibaba-inc.com/onetime/download/onetime-5.1.2.tgz", - "integrity": "sha1-0Oluu1awdHbfHdnEgG5SN5hcpF4=", - "dev": true, - "requires": { - "mimic-fn": "^2.1.0" - } - }, - "p-limit": { - "version": "2.3.0", - "resolved": "https://registry.npm.alibaba-inc.com/p-limit/download/p-limit-2.3.0.tgz", - "integrity": "sha1-PdM8ZHohT9//2DWTPrCG2g3CHbE=", - "dev": true, - "requires": { - "p-try": "^2.0.0" - } - }, - "p-locate": { - "version": "4.1.0", - "resolved": "https://registry.npm.alibaba-inc.com/p-locate/download/p-locate-4.1.0.tgz", - "integrity": "sha1-o0KLtwiLOmApL2aRkni3wpetTwc=", - "dev": true, - "requires": { - "p-limit": "^2.2.0" - } - }, - "p-try": { - "version": "2.2.0", - "resolved": "https://registry.npm.alibaba-inc.com/p-try/download/p-try-2.2.0.tgz", - "integrity": "sha1-yyhoVA4xPWHeWPr741zpAE1VQOY=", - "dev": true - }, - "path-exists": { - "version": "4.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/path-exists/download/path-exists-4.0.0.tgz", - "integrity": "sha1-UTvb4tO5XXdi6METfvoZXGxhtbM=", - "dev": true - }, - "path-key": { - "version": "3.1.1", - "resolved": "https://registry.npm.alibaba-inc.com/path-key/download/path-key-3.1.1.tgz", - "integrity": "sha1-WB9q3mWMu6ZaDTOA3ndTKVBU83U=", - "dev": true - }, - "path-parse": { - "version": "1.0.7", - "resolved": "https://registry.npm.alibaba-inc.com/path-parse/download/path-parse-1.0.7.tgz", - "integrity": "sha1-+8EUtgykKzDZ2vWFjkvWi77bZzU=", - "dev": true - }, - "pkg-dir": { - "version": "4.2.0", - "resolved": "https://registry.npm.alibaba-inc.com/pkg-dir/download/pkg-dir-4.2.0.tgz", - "integrity": "sha1-8JkTPfft5CLoHR2ESCcO6z5CYfM=", - "dev": true, - "requires": { - "find-up": "^4.0.0" - } - }, - "rechoir": { - "version": "0.7.1", - "resolved": "https://registry.npm.alibaba-inc.com/rechoir/download/rechoir-0.7.1.tgz", - "integrity": "sha1-lHipahyhNbXoj8An8D7pLWxkVoY=", - "dev": true, - "requires": { - "resolve": "^1.9.0" - } - }, - "resolve": { - "version": "1.20.0", - "resolved": "https://registry.npm.alibaba-inc.com/resolve/download/resolve-1.20.0.tgz", - "integrity": "sha1-YpoBP7P3B1XW8LeTXMHCxTeLGXU=", - "dev": true, - "requires": { - "is-core-module": "^2.2.0", - "path-parse": "^1.0.6" - } - }, - "resolve-cwd": { - "version": "3.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/resolve-cwd/download/resolve-cwd-3.0.0.tgz", - "integrity": "sha1-DwB18bslRHZs9zumpuKt/ryxPy0=", - "dev": true, - "requires": { - "resolve-from": "^5.0.0" - } - }, - "resolve-from": { - "version": "5.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/resolve-from/download/resolve-from-5.0.0.tgz", - "integrity": "sha1-w1IlhD3493bfIcV1V7wIfp39/Gk=", - "dev": true - }, - "shallow-clone": { - "version": "3.0.1", - "resolved": "https://registry.npm.alibaba-inc.com/shallow-clone/download/shallow-clone-3.0.1.tgz", - "integrity": "sha1-jymBrZJTH1UDWwH7IwdppA4C76M=", - "dev": true, - "requires": { - "kind-of": "^6.0.2" - } - }, - "shebang-command": { - "version": "2.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/shebang-command/download/shebang-command-2.0.0.tgz", - "integrity": "sha1-zNCvT4g1+9wmW4JGGq8MNmY/NOo=", - "dev": true, - "requires": { - "shebang-regex": "^3.0.0" - } - }, - "shebang-regex": { - "version": "3.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/shebang-regex/download/shebang-regex-3.0.0.tgz", - "integrity": "sha1-rhbxZE2HPsrYQ7AwexQzYtTEIXI=", - "dev": true - }, - "signal-exit": { - "version": "3.0.5", - "resolved": "https://registry.npm.alibaba-inc.com/signal-exit/download/signal-exit-3.0.5.tgz", - "integrity": "sha1-nj6MwMdamUcrRDIQM6dwLnc4JS8=", - "dev": true - }, - "strip-final-newline": { - "version": "2.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/strip-final-newline/download/strip-final-newline-2.0.0.tgz", - "integrity": "sha1-ibhS+y/L6Tb29LMYevsKEsGrWK0=", - "dev": true - }, - "webpack-cli": { - "version": "4.9.1", - "resolved": "https://registry.npm.alibaba-inc.com/webpack-cli/download/webpack-cli-4.9.1.tgz", - "integrity": "sha1-tkvoJeLRsTDyhcMUyqOxuppGMrM=", - "dev": true, - "requires": { - "@discoveryjs/json-ext": "^0.5.0", - "@webpack-cli/configtest": "^1.1.0", - "@webpack-cli/info": "^1.4.0", - "@webpack-cli/serve": "^1.6.0", - "colorette": "^2.0.14", - "commander": "^7.0.0", - "execa": "^5.0.0", - "fastest-levenshtein": "^1.0.12", - "import-local": "^3.0.2", - "interpret": "^2.2.0", - "rechoir": "^0.7.0", - "webpack-merge": "^5.7.3" - } - }, - "webpack-merge": { - "version": "5.8.0", - "resolved": "https://registry.npm.alibaba-inc.com/webpack-merge/download/webpack-merge-5.8.0.tgz", - "integrity": "sha1-Kznb8ir4d3atdEw5AiNzHTCmj2E=", - "dev": true, - "requires": { - "clone-deep": "^4.0.1", - "wildcard": "^2.0.0" - } - }, - "which": { - "version": "2.0.2", - "resolved": "https://registry.npm.alibaba-inc.com/which/download/which-2.0.2.tgz", - "integrity": "sha1-fGqN0KY2oDJ+ELWckobu6T8/UbE=", - "dev": true, - "requires": { - "isexe": "^2.0.0" - } - }, - "wildcard": { - "version": "2.0.0", - "resolved": "https://registry.npm.alibaba-inc.com/wildcard/download/wildcard-2.0.0.tgz", - "integrity": "sha1-p30g5SAMb6qsl55LOq3Hs91/j+w=", - "dev": true - } - } -} diff --git a/packages/graph/package.json b/packages/graph/package.json index 81960da..6c1754f 100644 --- a/packages/graph/package.json +++ b/packages/graph/package.json @@ -10,22 +10,22 @@ ], "files": [ "package.json", - "es", "lib", "dist", "LICENSE", "README.md" ], - "main": "lib/index.js", - "module": "es/index.js", + "main": "dist/index.min.js", + "module": "lib/index.js", "types": "lib/index.d.ts", "unpkg": "dist/index.min.js", "scripts": { - "build": "npm run clean && father build && npm run build:umd", + "build": "npm run clean && npm run build:esm && npm run build:umd", + "build:esm": "tsc", "build:umd": "webpack --config webpack.config.js --mode production", "dev:umd": "webpack --config webpack.dev.config.js --mode development", "ci": "npm run build && npm run coverage", - "clean": "rimraf es lib dist", + "clean": "rimraf lib dist", "coverage": "jest --coverage", "lint": "eslint --ext .js,.jsx,.ts,.tsx --format=pretty \"./\"", "lint:src": "eslint --ext .ts --format=pretty \"./src\"", @@ -54,20 +54,18 @@ "@types/jest": "^26.0.18", "@umijs/fabric": "^2.5.6", "babel-loader": "^8.2.2", - "father": "^2.30.0", "jest": "^26.6.3", - "jest-electron": "^0.1.11", "rimraf": "^3.0.2", "ts-jest": "^26.4.4", "ts-loader": "^8.0.14", "tslint": "^6.1.3", "typescript": "^4.1.3", - "webpack": "^5.17.0", - "webpack-cli": "^4.9.1", + "webpack": "^5.38.1", + "webpack-cli": "^5.0.2", "worker-loader": "^3.0.7" }, "dependencies": { "@antv/util": "^2.0.13", - "tslib": "^2.0.0" + "tslib": "^2.5.2" } } diff --git a/packages/graph/tsconfig.json b/packages/graph/tsconfig.json index 695d8ce..ba439d3 100644 --- a/packages/graph/tsconfig.json +++ b/packages/graph/tsconfig.json @@ -1,27 +1,23 @@ { "compilerOptions": { - "rootDir": "./", + "rootDir": "./src", + "composite": true, "module": "esnext", "declaration": true, "sourceMap": true, "target": "es5", "outDir": "lib", + "jsx": "react", "importHelpers": true, "moduleResolution": "node", "allowSyntheticDefaultImports": true, "resolveJsonModule": true, "esModuleInterop": true, "lib": ["esnext", "dom"], - "types": ["node", "jest"] + "experimentalDecorators": false /* Enables experimental support for ES7 decorators. */, + // "emitDecoratorMetadata": true, /* Enables experimental support for emitting type metadata for decorators. */ + "downlevelIteration": true, + "skipLibCheck": true }, - "include": ["src", "tests"], - "typedocOptions": { - "mode": "modules", - "out": "docs/api-ts", - "excludePrivate": true, - "excludeProtected": true, - "excludeExternals": true, - "plugin": "typedoc-plugin-markdown", - "theme": "docusaurus2" - } -} \ No newline at end of file + "include": ["src/**/*"] +} diff --git a/packages/webgpu-graph/.fatherrc.js b/packages/webgpu-graph/.fatherrc.js deleted file mode 100644 index 2b646ff..0000000 --- a/packages/webgpu-graph/.fatherrc.js +++ /dev/null @@ -1,5 +0,0 @@ -export default { - entry: './src/index.ts', - esm: 'babel', - cjs: 'babel' -}; \ No newline at end of file diff --git a/packages/webgpu-graph/src/traversal/bfs.ts b/packages/webgpu-graph/src/traversal/bfs.ts deleted file mode 100644 index fa74bb5..0000000 --- a/packages/webgpu-graph/src/traversal/bfs.ts +++ /dev/null @@ -1,14 +0,0 @@ -import type { WebGLRenderer } from '@antv/g-webgl'; -import { Kernel, BufferUsage } from '@antv/g-plugin-gpgpu'; -import { GraphData } from '../types'; -import { convertGraphData2CSC } from '../util'; - -/** - * Scalable GPU Graph Traversal - * @see https://research.nvidia.com/publication/scalable-gpu-graph-traversal - * @see https://github.com/rafalk342/bfs-cuda - * @see https://github.com/kaletap/bfs-cuda-gpu - */ -export async function bfs(device: WebGLRenderer.Device, graphData: GraphData) { - -} \ No newline at end of file diff --git a/packages/webgpu-graph/src/types.ts b/packages/webgpu-graph/src/types.ts deleted file mode 100644 index 9658d36..0000000 --- a/packages/webgpu-graph/src/types.ts +++ /dev/null @@ -1,27 +0,0 @@ -export interface NodeConfig { - id: string; - clusterId?: string; - [key: string]: any; -} - -export interface EdgeConfig { - source: string; - target: string; - weight?: number; - [key: string]: any; -} - -export interface GraphData { - nodes?: NodeConfig[]; - edges?: EdgeConfig[]; -} - -export interface CSC { - V: number[]; - E: number[]; - I: number[]; - From: number[]; - To: number[]; - nodeId2IndexMap: Record; - edges: EdgeConfig[]; -} diff --git a/packages/webgpu-graph/tsconfig.json b/packages/webgpu-graph/tsconfig.json deleted file mode 100644 index 695d8ce..0000000 --- a/packages/webgpu-graph/tsconfig.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "compilerOptions": { - "rootDir": "./", - "module": "esnext", - "declaration": true, - "sourceMap": true, - "target": "es5", - "outDir": "lib", - "importHelpers": true, - "moduleResolution": "node", - "allowSyntheticDefaultImports": true, - "resolveJsonModule": true, - "esModuleInterop": true, - "lib": ["esnext", "dom"], - "types": ["node", "jest"] - }, - "include": ["src", "tests"], - "typedocOptions": { - "mode": "modules", - "out": "docs/api-ts", - "excludePrivate": true, - "excludeProtected": true, - "excludeExternals": true, - "plugin": "typedoc-plugin-markdown", - "theme": "docusaurus2" - } -} \ No newline at end of file diff --git a/packages/webgpu-graph/yarn.lock b/packages/webgpu-graph/yarn.lock deleted file mode 100644 index c7b2e25..0000000 --- a/packages/webgpu-graph/yarn.lock +++ /dev/null @@ -1,23604 +0,0 @@ -# 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"packages/*" diff --git a/site/benchmark/index.html b/site/benchmark/index.html new file mode 100644 index 0000000..7a1e793 --- /dev/null +++ b/site/benchmark/index.html @@ -0,0 +1,187 @@ + + + + + + + Benchmarks of graph algorithms + + + +
+ Choose a dataset: + +

+
+ +
+ Choose an algorithm: + +
+ + + +
+
+
graphology
+
+ +
+
+
@antv/algorithm
+
+ +
+
+
@antv/webgpu-graph
+
+ +
+
+
@antv/graph-wasm
+
+ +
+
+ + + + diff --git a/site/benchmark/main.ts b/site/benchmark/main.ts new file mode 100644 index 0000000..d875fd6 --- /dev/null +++ b/site/benchmark/main.ts @@ -0,0 +1,111 @@ +import { WebGPUGraph } from "../../packages/graph-gpu/src"; +import { loadDatasets } from "../datasets"; +import { TestName } from "../types"; +import { + graphology as graphologyPageRank, + webgpu as webgpuPageRank, +} from "./page-rank"; +import { graphology as graphologySSSP, webgpu as webgpuSSSP } from "./sssp"; + +const TestsConfig = [ + { + name: TestName.GRAPHOLOGY, + }, + { + name: TestName.ANTV_ALGORITHM, + }, + { + name: TestName.ANTV_WEBGPU_GRAPH, + }, + { + name: TestName.ANTV_WEBGPU_GRAPH, + }, +]; + +const $dataset = document.getElementById("dataset") as HTMLSelectElement; +const $datasetDesc = document.getElementById("dataset-desc") as HTMLSpanElement; +const $algorithm = document.getElementById("algorithm") as HTMLSelectElement; +const $run = document.getElementById("run") as HTMLButtonElement; +const $results = TestsConfig.map(({ name }) => { + const $div = document.getElementById(name) as HTMLDivElement; + return [$div.querySelector(".console")!, $div.querySelector(".time")!]; +}); + +(async () => { + $run.innerHTML = "Loading..."; + $run.disabled = true; + console.time("Load datasets"); + const datasets = await loadDatasets(); + $dataset.onchange = () => { + $datasetDesc.innerHTML = datasets[$dataset.value].desc; + }; + console.timeEnd("Load datasets"); + + // initialize WebGPU context + const graph = new WebGPUGraph(); + + $run.innerHTML = "Run layouts"; + $run.disabled = false; + + const layoutConfig: any = [ + { + name: TestName.GRAPHOLOGY, + methods: { + pageRank: graphologyPageRank, + sssp: graphologySSSP, + }, + }, + { + name: TestName.ANTV_ALGORITHM, + methods: { + // pageRank: graphologyForceatlas2, + // sssp: graphologyFruchterman, + }, + }, + { + name: TestName.ANTV_WEBGPU_GRAPH, + methods: { + pageRank: webgpuPageRank, + sssp: webgpuSSSP, + }, + }, + { + name: TestName.ANTV_GRAPH_WASM, + methods: { + // pageRank: webgpuPageRank, + // sssp: webgpuSSSP, + }, + }, + ]; + + $run.onclick = async () => { + const dataset = datasets[$dataset.value]; + const algorithmName = $algorithm.value; + let options = null; + if (algorithmName === "sssp") { + const graph = dataset[TestName.ANTV_WEBGPU_GRAPH]; + options = graph.getAllNodes()[1].id; + } + + await Promise.all( + layoutConfig.map(async ({ name, methods }: any, i: number) => { + if (methods[algorithmName]) { + const start = performance.now(); + const result = await methods[algorithmName]( + dataset[name], + options, + graph + ); + $results[i][1].innerHTML = `${(performance.now() - start).toFixed( + 2 + )}ms`; + + $results[i][0].innerHTML = JSON.stringify(result); + } else { + $results[i][0].innerHTML = ""; + $results[i][1].innerHTML = "-"; + } + }) + ); + }; +})(); diff --git a/site/benchmark/page-rank.ts b/site/benchmark/page-rank.ts new file mode 100644 index 0000000..8e3246b --- /dev/null +++ b/site/benchmark/page-rank.ts @@ -0,0 +1,48 @@ +import pagerank from "graphology-metrics/centrality/pagerank"; +import { WebGPUGraph } from "../../packages/graph-gpu/src"; + +interface Options { + alpha: number; + maxIterations: number; + tolerance: number; +} + +const DEFAULT_OPTIONS: Options = { + alpha: 0.85, + maxIterations: 1000, + tolerance: 1e-5, +}; + +export async function graphology(graph: any, options: Partial) { + const result = pagerank(graph, { + ...DEFAULT_OPTIONS, + ...options, + getEdgeWeight: "weight", + }); + + const r = Object.keys(result).map((key) => ({ + id: key, + score: result[key], + })); + + return r.sort((a, b) => b.score - a.score).slice(0, 10); // {id: 'Valjean', score: 0.1} +} + +export async function webgpu( + graph: any, + options: Partial, + webgpuGraph: WebGPUGraph +) { + const { alpha, tolerance, maxIterations } = { + ...DEFAULT_OPTIONS, + ...options, + }; + const result = await webgpuGraph.pageRank( + graph, + tolerance, + alpha, + maxIterations + ); + + return result.slice(0, 10); // {id: 'Valjean', score: 0.1} +} diff --git a/site/benchmark/sssp.ts b/site/benchmark/sssp.ts new file mode 100644 index 0000000..77c7572 --- /dev/null +++ b/site/benchmark/sssp.ts @@ -0,0 +1,16 @@ +import { dijkstra } from "graphology-shortest-path"; +import { WebGPUGraph } from "../../packages/graph-gpu/src"; + +export async function graphology(graph: any, source: string) { + return dijkstra.singleSource(graph, source, "weight"); +} + +export async function webgpu( + graph: any, + source: string, + webgpuGraph: WebGPUGraph +) { + const result = await webgpuGraph.sssp(graph, source, "weight"); + + return result.filter((r) => r.distance !== 1000000); +} diff --git a/site/datasets.ts b/site/datasets.ts new file mode 100644 index 0000000..c65d72c --- /dev/null +++ b/site/datasets.ts @@ -0,0 +1,169 @@ +import Graph from "graphology"; +import { Graph as AntvGraph } from "@antv/graphlib"; +import { clusters } from "graphology-generators/random"; +import { TestName } from "./types"; + +export const loadDatasets = async () => { + const datasets: Record = {}; + + const loadRandomClusters = () => { + const NODES = 1000; + const EDGES = 2000; + + // Use graphology generator. + const graph = clusters(Graph, { + order: NODES, + size: EDGES, + clusters: 5, + }); + graph.edges().forEach(function(edge, i) { + // graph.setEdgeAttribute(edge, "weight", i > EDGES / 2 ? 1 : 100); + graph.setEdgeAttribute(edge, "weight", 1); + }); + // graph.nodes().forEach(function (node) { + // graph.setNodeAttribute(node, "x", Math.random() * CANVAS_SIZE); + // graph.setNodeAttribute(node, "y", Math.random() * CANVAS_SIZE); + + // if (dimensions === 3) { + // graph.setNodeAttribute(node, "z", Math.random() * CANVAS_SIZE); + // } + // }); + + const antvgraph = graphology2antv(graph); + + return { + desc: + "Creates a graph with the desired number of nodes & edges and having a given number of clusters. Generated by Graphology Generators.", + [TestName.GRAPHOLOGY]: graph, + [TestName.ANTV_ALGORITHM]: antvgraph, + [TestName.ANTV_WEBGPU_GRAPH]: antvgraph, + }; + }; + + const loadG6JSON = (url: string, desc: string) => { + return async () => { + const result = await fetch(url); + const oldG6GraphFormat = await result.json(); + + // format old G6 graph format to @antv/graphlib + // assign random positions + const nodes: any[] = []; + const edges: any[] = []; + const uniqueNodes = new Set(); + oldG6GraphFormat.nodes.forEach((node: any, i: number) => { + // remove duplicated nodes + if (!uniqueNodes.has(node.id)) { + uniqueNodes.add(node.id); + + // clear + node.x = undefined; + node.y = undefined; + node.z = undefined; + + nodes.push({ + id: node.id, + data: { x: node.x, y: node.y, z: node.z }, + }); + } + }); + oldG6GraphFormat.edges.forEach((edge: any, i: number) => { + if (edge.id === undefined) { + edge.id = `e${i}`; + } + if (edge.weight === undefined || edge.weight === null) { + edge.weight = 1; + } else { + edge.weight = Number(edge.weight); + } + + edges.push({ + id: edge.id, + source: edge.source, + target: edge.target, + data: { weight: edge.weight }, + }); + }); + + const antvGraphModel = new AntvGraph({ + nodes, + edges, + }); + + const graphlibModel = antv2graphology(antvGraphModel); + return { + desc, + [TestName.GRAPHOLOGY]: graphlibModel, + [TestName.ANTV_ALGORITHM]: oldG6GraphFormat, + [TestName.ANTV_WEBGPU_GRAPH]: antvGraphModel, + }; + }; + }; + + const [ + randomClusters, + relations, + netscience, + eva, + regions, + ] = await Promise.all([ + loadRandomClusters(), + loadG6JSON( + "https://gw.alipayobjects.com/os/antvdemo/assets/data/relations.json", + 'A small dataset of "relations" between people.' + )(), + loadG6JSON( + "https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json", + "Netscience with 1589 nodes & 2742 edges." + )(), + loadG6JSON( + "https://gw.alipayobjects.com/os/basement_prod/0b9730ff-0850-46ff-84d0-1d4afecd43e6.json", + "Eva with 8322 nodes & 5421 edges." + )(), + loadG6JSON( + "https://gw.alipayobjects.com/os/basement_prod/7bacd7d1-4119-4ac1-8be3-4c4b9bcbc25f.json", + "A dataset for regions on earth." + )(), + ]); + + datasets["random-clusters"] = randomClusters; + datasets.relations = relations; + datasets.netscience = netscience; + datasets.eva = eva; + datasets.regions = regions; + + return datasets; +}; + +const graphology2antv = (graph: any): AntvGraph => { + return new AntvGraph({ + nodes: graph.nodes().map((id: any) => ({ + id, + data: { + x: graph.getNodeAttribute(id, "x"), + y: graph.getNodeAttribute(id, "y"), + z: graph.getNodeAttribute(id, "z"), + }, + })), + edges: graph.edges().map((id: any) => ({ + id, + source: graph.source(id), + target: graph.target(id), + data: { + weight: graph.getEdgeAttribute(id, "weight"), + }, + })), + }); +}; + +const antv2graphology = (graph: AntvGraph) => { + const g = new Graph(); + graph.getAllNodes().forEach(({ id, data: { x, y, z } }: any) => { + if (!g.hasNode(id)) { + g.addNode(id, { x, y, z }); + } + }); + graph.getAllEdges().forEach(({ source, target, data: { weight } }: any) => { + g.addEdge(source, target, { weight }); + }); + return g; +}; diff --git a/site/index.html b/site/index.html new file mode 100644 index 0000000..2258375 --- /dev/null +++ b/site/index.html @@ -0,0 +1,19 @@ + + + + + + + AntV Algorithm + + + Here are some examples using + @antv/algorithm. +
    +
  • + Benchmarks between CPU, WASM and GPU + implementation. +
    • +
+ + diff --git a/site/types.ts b/site/types.ts new file mode 100644 index 0000000..75f046e --- /dev/null +++ b/site/types.ts @@ -0,0 +1,6 @@ +export enum TestName { + GRAPHOLOGY = "graphology", + ANTV_ALGORITHM = "@antv/algorithm", + ANTV_WEBGPU_GRAPH = "@antv/webgpu-graph", + ANTV_GRAPH_WASM = "@antv/graph-wasm", +} diff --git a/vite.config.js b/vite.config.js new file mode 100644 index 0000000..3a81ae7 --- /dev/null +++ b/vite.config.js @@ -0,0 +1,19 @@ +import { resolve } from "path"; +import { defineConfig } from "vite"; + +export default defineConfig({ + root: "./site/", + server: { + port: 8080, + open: "/", + }, + base: "/algorithm/", + build: { + rollupOptions: { + input: { + main: resolve(__dirname, "site/index.html"), + benchmark: resolve(__dirname, "site/benchmark/index.html"), + }, + }, + }, +}); From 1a8f3c10198620537d8e946cc8fbe959cdfc6bb6 Mon Sep 17 00:00:00 2001 From: xiaoiver Date: Thu, 13 Jul 2023 15:29:18 +0800 Subject: [PATCH 02/15] feat: impl page rank (#64) * feat: impl page rank * feat: sssp impl * fix: use rayon to spawn threads instead of std:threads * chore: commit pnpm lockfile * feat: js impl fo louvain * feat: rust impl for louvain --- .github/workflows/ci.yml | 31 + .gitignore | 6 +- .husky/commit-msg | 4 + .husky/pre-commit | 4 + CHANGELOG.md | 93 - README-zh_CN.md | 40 - README.md | 87 +- .../data/cluster-origin-properties-data.json | 404 + .../unit/findPath.spec.ts | 59 +- __tests__/unit/louvain.spec.ts | 67 + __tests__/unit/pageRank.spec.ts | 172 + benchmarks/louvain.test.js | 54 + benchmarks/util.js | 60 + jest.config.js | 30 + package.json | 70 +- packages/graph-gpu/.prettierrc.js | 5 - packages/graph-gpu/package.json | 19 +- packages/graph-gpu/src/WebGPUGraph.ts | 6 +- packages/graph-gpu/src/index.ts | 1 + .../graph-gpu/src/link-analysis/pageRank.ts | 28 +- packages/graph-gpu/src/traversal/sssp.ts | 2 +- packages/graph-gpu/tsconfig.json | 1 + packages/graph-rust/Cargo.toml | 39 +- packages/graph-rust/README.md | 3 + packages/graph-rust/rustfmt.toml | 8 + .../graph-rust/src/graph_builder/builder.rs | 341 + .../graph-rust/src/graph_builder/compat.rs | 109 + .../graph-rust/src/graph_builder/graph/csr.rs | 873 + .../graph-rust/src/graph_builder/graph/mod.rs | 1 + .../graph-rust/src/graph_builder/graph_ops.rs | 627 + .../graph-rust/src/graph_builder/index.rs | 103 + .../src/graph_builder/input/edgelist.rs | 258 + .../graph-rust/src/graph_builder/input/mod.rs | 93 + packages/graph-rust/src/graph_builder/mod.rs | 171 + .../graph-rust/src/graph_builder/prelude.rs | 32 + packages/graph-rust/src/lib.rs | 8 + packages/graph-rust/src/louvain.rs | 604 + packages/graph-rust/src/page_rank.rs | 153 + packages/graph-rust/src/prelude.rs | 6 + packages/graph-rust/src/sssp.rs | 264 + packages/graph-rust/src/utils.rs | 1 + packages/graph-wasm/.gitignore | 5 - packages/graph-wasm/Cargo.lock | 182 +- packages/graph-wasm/Cargo.toml | 2 +- packages/graph-wasm/README.md | 119 + packages/graph-wasm/package.json | 9 - packages/graph-wasm/rust-src/lib.rs | 72 +- packages/graph-wasm/src/interface.ts | 27 +- packages/graph-wasm/src/wasm-worker.js | 59 +- packages/graph-wasm/tsconfig.json | 25 + packages/graph/.prettierrc.js | 5 - packages/graph/jest.config.js | 15 - packages/graph/package.json | 45 +- packages/graph/src/adjMatrix.ts | 29 + packages/graph/src/adjacent-matrix.ts | 38 - packages/graph/src/asyncIndex.ts | 69 - packages/graph/src/bfs.ts | 89 - packages/graph/src/connected-component.ts | 113 - packages/graph/src/constants/time.ts | 2 - packages/graph/src/cosine-similarity.ts | 27 - packages/graph/src/degree.ts | 51 - packages/graph/src/detect-cycle.ts | 346 - packages/graph/src/dfs.ts | 76 - packages/graph/src/dijkstra.ts | 96 +- .../graph/src/{find-path.ts => findPath.ts} | 42 +- packages/graph/src/floydWarshall.ts | 34 - packages/graph/src/gSpan/gSpan.ts | 954 - packages/graph/src/gSpan/struct.ts | 114 - packages/graph/src/gaddi.ts | 1211 - packages/graph/src/i-louvain.ts | 28 - packages/graph/src/iLouvain.ts | 24 + packages/graph/src/index.ts | 92 +- packages/graph/src/k-core.ts | 37 - packages/graph/src/k-means.ts | 216 - packages/graph/src/label-propagation.ts | 151 - packages/graph/src/louvain.ts | 241 +- packages/graph/src/mts.ts | 116 - packages/graph/src/nodes-cosine-similarity.ts | 50 - packages/graph/src/pageRank.ts | 64 +- packages/graph/src/structs/binary-heap.ts | 90 - packages/graph/src/structs/linked-list.ts | 245 - packages/graph/src/structs/queue.ts | 46 - packages/graph/src/structs/stack.ts | 65 - packages/graph/src/structs/union-find.ts | 45 - packages/graph/src/types.ts | 105 +- packages/graph/src/util.ts | 59 - .../{utils/data-preprocessing.ts => utils.ts} | 87 +- packages/graph/src/utils/node-properties.ts | 78 - packages/graph/src/{utils => }/vector.ts | 65 +- packages/graph/src/workers/algorithm.ts | 16 - packages/graph/src/workers/constant.ts | 31 - packages/graph/src/workers/createWorker.ts | 33 - packages/graph/src/workers/index.ts | 253 - packages/graph/src/workers/index.worker.ts | 27 - .../tests/unit/adjacent-matrix-async-spec.ts | 116 - .../graph/tests/unit/adjacent-matrix-spec.ts | 114 - packages/graph/tests/unit/bfs-spec.ts | 175 - .../unit/connected-component-async-spec.ts | 112 - .../tests/unit/connected-component-spec.ts | 106 - .../graph/tests/unit/cosineSimilarity-spec.ts | 41 - .../tests/unit/data/cluster-origin-data.json | 1 - .../data/cluster-origin-properties-data.json | 370 - packages/graph/tests/unit/data/test-data.ts | 38635 ---------------- .../graph/tests/unit/degree-async-spec.ts | 119 - packages/graph/tests/unit/degree-spec.ts | 139 - .../tests/unit/detect-cycle-async-spec.ts | 74 - .../graph/tests/unit/detect-cycle-spec.ts | 508 - packages/graph/tests/unit/dfs-spec.ts | 170 - .../graph/tests/unit/find-path-async-spec.ts | 132 - .../tests/unit/floydWarshall-async-spec.ts | 110 - .../graph/tests/unit/floydWarshall-spec.ts | 108 - packages/graph/tests/unit/gSpan-spec.ts | 483 - packages/graph/tests/unit/gaddi-async-spec.ts | 388 - packages/graph/tests/unit/gaddi-spec.ts | 1110 - packages/graph/tests/unit/kMeans-spec.ts | 236 - .../unit/label-propagation-async-spec.ts | 89 - .../tests/unit/label-propagation-spec.ts | 54 - packages/graph/tests/unit/linked-list-spec.ts | 88 - .../graph/tests/unit/louvain-async-spec.ts | 67 - packages/graph/tests/unit/louvain-spec.ts | 67 - packages/graph/tests/unit/mst-spec.ts | 84 - .../tests/unit/nodesCosineSimilarity-spec.ts | 104 - .../graph/tests/unit/pagerank-async-spec.ts | 138 - packages/graph/tests/unit/pagerank-spec.ts | 137 - packages/graph/tests/unit/queue-spec.ts | 65 - packages/graph/tests/unit/stack-spec.ts | 85 - packages/graph/tests/unit/util-spec.ts | 116 - packages/graph/tests/unit/utils.ts | 20 - packages/graph/tsconfig.json | 1 + packages/graph/webpack.config.js | 64 +- pnpm-lock.yaml | 7658 +++ site/benchmark/index.html | 10 +- site/benchmark/louvain.ts | 44 + site/benchmark/main.ts | 80 +- site/benchmark/page-rank.ts | 58 +- site/benchmark/sssp.ts | 39 +- site/benchmark/util.ts | 20 + site/data/undirected1000.json | 1 + site/data/undirected500.json | 1 + site/datasets.ts | 45 +- site/index.html | 2 +- site/types.ts | 4 +- tsconfig.json | 12 + tslint.json | 21 + vite.config.js | 14 + 145 files changed, 13363 insertions(+), 50129 deletions(-) create mode 100644 .github/workflows/ci.yml create mode 100644 .husky/commit-msg create mode 100644 .husky/pre-commit delete mode 100644 CHANGELOG.md delete mode 100644 README-zh_CN.md create mode 100644 __tests__/data/cluster-origin-properties-data.json rename packages/graph/tests/unit/find-path-spec.ts => __tests__/unit/findPath.spec.ts (64%) create mode 100644 __tests__/unit/louvain.spec.ts create mode 100644 __tests__/unit/pageRank.spec.ts create mode 100644 benchmarks/louvain.test.js create mode 100644 benchmarks/util.js create mode 100644 jest.config.js delete mode 100644 packages/graph-gpu/.prettierrc.js create mode 100644 packages/graph-rust/README.md create mode 100644 packages/graph-rust/rustfmt.toml create mode 100644 packages/graph-rust/src/graph_builder/builder.rs create mode 100644 packages/graph-rust/src/graph_builder/compat.rs create mode 100644 packages/graph-rust/src/graph_builder/graph/csr.rs create mode 100644 packages/graph-rust/src/graph_builder/graph/mod.rs create mode 100644 packages/graph-rust/src/graph_builder/graph_ops.rs create mode 100644 packages/graph-rust/src/graph_builder/index.rs create mode 100644 packages/graph-rust/src/graph_builder/input/edgelist.rs create mode 100644 packages/graph-rust/src/graph_builder/input/mod.rs create mode 100644 packages/graph-rust/src/graph_builder/mod.rs create mode 100644 packages/graph-rust/src/graph_builder/prelude.rs create mode 100644 packages/graph-rust/src/lib.rs create mode 100644 packages/graph-rust/src/louvain.rs create mode 100644 packages/graph-rust/src/page_rank.rs create mode 100644 packages/graph-rust/src/prelude.rs create mode 100644 packages/graph-rust/src/sssp.rs create mode 100644 packages/graph-rust/src/utils.rs delete mode 100644 packages/graph-wasm/.gitignore create mode 100644 packages/graph-wasm/tsconfig.json delete mode 100644 packages/graph/.prettierrc.js delete mode 100644 packages/graph/jest.config.js create mode 100644 packages/graph/src/adjMatrix.ts delete mode 100644 packages/graph/src/adjacent-matrix.ts delete mode 100644 packages/graph/src/asyncIndex.ts delete mode 100644 packages/graph/src/bfs.ts delete mode 100644 packages/graph/src/connected-component.ts delete mode 100644 packages/graph/src/constants/time.ts delete mode 100644 packages/graph/src/cosine-similarity.ts delete mode 100644 packages/graph/src/degree.ts delete mode 100644 packages/graph/src/detect-cycle.ts delete mode 100644 packages/graph/src/dfs.ts rename packages/graph/src/{find-path.ts => findPath.ts} (59%) delete mode 100644 packages/graph/src/floydWarshall.ts delete mode 100644 packages/graph/src/gSpan/gSpan.ts delete 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/dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,31 @@ +name: CI + +on: ["push", "pull_request"] + +jobs: + ci: + runs-on: ubuntu-20.04 + strategy: + matrix: + node-version: [15] + steps: + - uses: actions/checkout@v3 + - uses: pnpm/action-setup@v2 + with: + version: 7 + - name: Use Node.js ${{ matrix.node-version }} + uses: actions/setup-node@v3 + with: + node-version: ${{ matrix.node-version }} + cache: "pnpm" + - name: Install dependencies + run: pnpm install + - name: lint + run: | + pnpm lint + - name: build + run: | + pnpm build:ci + - name: test + run: | + pnpm test diff --git a/.gitignore b/.gitignore index 04afe08..bdd640c 100644 --- a/.gitignore +++ b/.gitignore @@ -11,5 +11,9 @@ es dist *.pem !mock-cert.pem -pnpm-lock.yaml tsconfig.tsbuildinfo +target +Cargo.lock +pkg +pkg-node +pkg-parallel diff --git a/.husky/commit-msg b/.husky/commit-msg new file mode 100644 index 0000000..62aa592 --- /dev/null +++ b/.husky/commit-msg @@ -0,0 +1,4 @@ +#!/bin/sh +. "$(dirname "$0")/_/husky.sh" + +npx --no-install commitlint --edit "$1" \ No newline at end of file diff --git a/.husky/pre-commit b/.husky/pre-commit new file mode 100644 index 0000000..c37466e --- /dev/null +++ b/.husky/pre-commit @@ -0,0 +1,4 @@ +#!/bin/sh +. "$(dirname "$0")/_/husky.sh" + +npx lint-staged \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md deleted file mode 100644 index 77bd6ce..0000000 --- a/CHANGELOG.md +++ /dev/null @@ -1,93 +0,0 @@ -# ChangeLog -#### 0.1.25 - -- feat: Optimized data preprocessing coding - when the feature values are all numerical, use the original values (plus normalization), and do not use one-hot coding -#### 0.1.24 - -- fix: i-louvain without cluster problem; -#### 0.1.23 - -- perf: k-means algorithm: set K to minimum -#### 0.1.22 - -- fix: k-means algorithm, perf: louvain -- support specified parameters such as propertyKey,involvedKeys and uninvolvedKeys -#### 0.1.21 - -- perf: k-means algorithm -- Optimize parameters and return - -#### 0.1.20 - -- feat: add k-means algorithm for nodes clustering - -#### 0.1.19 - -- fix: GADDI matched failed problem; - -#### 0.1.18 - -- feat: add one-hot data preprocessing; - -#### 0.1.17 - -- feat: add consine-similarity algorithm and nodes-consine-similarity algorithm; - -#### 0.1.16 - -- feat: add i-louvain based on louvain according to academic; - -#### 0.1.15 - -- feat: k-core algorithm; - -#### 0.1.14 - -- fix: GADDI with proper begin p node; -- feat: louvain with property similarity measure - -#### 0.1.10 - -- fix: GADDI with better accuracy; - -#### 0.1.9 - -- chore: separate sync and async functions into different entries; - -#### 0.1.8 - -- fix: CPU usage increases due to 0.1.3-beta ~ 0.1.3 with publicPath configuration; -- fix: CPU usage increases due to 0.1.6 ~ 0.17 with browser output; -- feat: export fix: export detectAllCycles, detectAllDirectedCycle, detectAllUndirectedCycle; - -#### 0.1.6 - -- fix: louvain with increased clusterId and node with correct new clusterId; - -#### 0.1.5 - -- fix: worker async problem; -- chore: unify allPath and allPaths; - -#### 0.1.2 - -- fix: failed to find result problem in dijkstra; - -#### 0.1.1 - -- fix: shortestPath with wrong result; - -#### 0.1.0 - -- fix: findShortestPath undefined is not interatable; - -#### 0.1.0-beta.3 - -- fix: cannot read degree of undefined problem of GADDI; - -#### 0.1.0-beta - -- feat: worker for algorithms; -- feat: gaddi for graph pattern matching; - -#### 0.0.7 - -- feat: dijkstra supports finding multiple shortest paths; diff --git a/README-zh_CN.md b/README-zh_CN.md deleted file mode 100644 index 59bfce3..0000000 --- a/README-zh_CN.md +++ /dev/null @@ -1,40 +0,0 @@ -### AntV Algorithm - -AntV 算法包，包括图算法及其他各类算法。 - -graph 包下面包括的都是图算法。 - -AntV 共支持以下图算法： -- **社区发现** - - k-core: K-Core社区发现算法 -- 找到符合指定核心度K的密切相关子图结构 - - louvain: LOUVAIN 算法 -- 根据模块度划分社区 - - i-louvain: I-LOUVAIN 算法 -- 根据模块度和惯性模块度（属性相似度）划分社区 - - labelPropagation: 标签传播算法 - - minimumSpanningTree: 图的最小生成树 - -- **节点聚类** - - k-means: K-Means算法 - 根据节点之间的距离将节点分为K个簇 - -- **相似性** - - cosineSimilarity: 余弦相似度算法 -- 计算两个元素的余弦相似度 - - nodesCosineSimilarity: 节点余弦相似度算法 -- 计算节点与种子节点之间的余弦相似度 - - -- **中心性** - - pageRank: 节点排序的页面排序算法 - - degree: 计算节点的入度、出度和总度 - -- **路径** - - dijkstra: Dijkstra 最短路径算法 - - findPath: 通过Dijkstra找到两个节点的最短路径和所有路径 - - floydWarshall: 弗洛伊德最短路径算法 - -- **其它** - - neighbors: 在图中查找节点的邻居 - - GADDI: 图结构和语义模式匹配算法 - - detectCycle: 环路检测 - - dfs: D深度优先遍历 - - adjacentMatrix: 邻接矩阵 - - connectedComponent: 联通子图 - -并支持在 web-worker 中计算上述算法 diff --git a/README.md b/README.md index 54dd385..428e1dc 100644 --- a/README.md +++ b/README.md @@ -1,35 +1,64 @@ -### AntV Algorithm +

+AntV Algorithm +

-It is an algorithm package of AntV, mainly includes graph related algorithms: -- **Community Detection** - - k-core: K-Core community detection algorithm -- Find the closely related subgraph structure that conforms to the specified core degree K - - louvain: LOUVAIN algorithm -- Divide communities according to Modularity - - i-louvain: I-LOUVAIN algorithm -- Divide communities according to Modularity and Inertial Modularity (properties similarity) - - labelPropagation: Label Propagation(LP) clustering algorithm - - minimumSpanningTree: Generate the minimum spanning tree for a graph +[![Build Status](https://github.com/antvis/algorithm/workflows/build/badge.svg?branch=next)](https://github.com/antvis//actions) +[![Coverage Status](https://img.shields.io/coveralls/github/antvis/algorithm/next.svg)](https://coveralls.io/github/antvis/algorithm?branch=next) +[![npm Download](https://img.shields.io/npm/dm/@antv/graph.svg)](https://www.npmjs.com/package/@antv/graph) +[![npm License](https://img.shields.io/npm/l/@antv/graph.svg)](https://www.npmjs.com/package/@antv/graph) -- **nodes clustering** - - k-means: K-Means algorithm - Cluster nodes into K clusters according to the distance between node +- [@antv/graph](./packages/graph/README.md) [![npm Version](https://img.shields.io/npm/v/@antv/graph/alpha)](https://www.npmjs.com/package/@antv/graph) Implemented with TypeScript. [Online Demo](https://observablehq.com/d/2db6b0cc5e97d8d6) +- [@antv/graph-rust](./packages/graph-rust/README.md) Implemented with Rust. +- [@antv/graph-wasm](./packages/graph-wasm/README.md) [![npm Version](https://img.shields.io/npm/v/@antv/graph-wasm)](https://www.npmjs.com/package/@antv/graph-wasm) Provide a WASM binding of `@antv/graph-rust`. [Online Demo](https://observablehq.com/d/288c16a54543a141) +- [@antv/graph-gpu](./packages/graph-gpu/README.md) [![npm Version](https://img.shields.io/npm/v/@antv/graph-gpu)](https://www.npmjs.com/package/@antv/graph-gpu) Accelerate some parallelizable algorithms such as Fruchterman with WebGPU which has a better performance under large amount of data. -- **Similarity** - - cosineSimilarity: Cosine Similarity algorithm -- Calculate cosine similarity - - nodesCosineSimilarity: Nodes Cosine Similarity algorithm -- Calculate the cosine similarity between other nodes and seed node +It is an algorithm package of AntV, mainly includes graph related algorithms: - **Centrality** - pageRank: page rank algorithm for nodes ranking - - degree: calculate the in degree, out degree, and total degree for nodes - -- **Path** - - dijkstra: Dijkstra shortest path algorithm - - findPath: Find the shortest paths and all paths for two nodes by Dijkstra - - floydWarshall: Floyd Warshall shortest path algorithm - -- **Other** - - neighbors: Find the neighbors for a node in the graph - - GADDI: graph structural and semantic pattern matching algorithm - - detectCycle: Detect the cycles of the graph data - - dfs: Depth-First search algorithm - - adjacentMatrix: calculate the adjacency matrix for graph data - - connectedComponent: Calculate the connected components for graph data - -All the algorithms above supports to be calculated with web-worker. \ No newline at end of file + +## Development + +We use [Vite](https://vitejs.dev/) to start a dev server: + +```bash +$ pnpm dev +``` + +## Test + +Run all the test cases with Jest: + +```bash +$ pnpm test +``` + +## Publish + +Using Changesets with pnpm: https://pnpm.io/next/using-changesets + +The generated markdown files in the .changeset directory should be committed to the repository. + +```bash +pnpm changeset +``` + +This will bump the versions of the packages previously specified with pnpm changeset (and any dependents of those) and update the changelog files. + +```bash +pnpm changeset version +``` + +Commit the changes. This command will publish all packages that have bumped versions not yet present in the registry. + +```bash +pnpm publish -r +``` + +If you want to publish versions for test: + +```bash +pnpm changeset pre enter alpha +pnpm changeset pre enter beta +pnpm changeset pre enter rc +``` diff --git a/__tests__/data/cluster-origin-properties-data.json b/__tests__/data/cluster-origin-properties-data.json new file mode 100644 index 0000000..f174b90 --- /dev/null +++ b/__tests__/data/cluster-origin-properties-data.json @@ -0,0 +1,404 @@ +{ + "nodes": [ + { + "id": "node-0", + "label": "node-0", + "data": { + "city": "110000", + "age": 20, + "amount": 100, + "wifi": "wifi-0" + } + }, + { + "id": "node-1", + "label": "node-1", + "data": { + "city": "110000", + "age": 22, + "amount": 100, + "wifi": "wifi-1" + } + }, + { + "id": "node-2", + "label": "node-2", + "data": { + "city": "110000", + "age": 20, + "amount": 100, + "wifi": "wifi-2" + } + }, + { + "id": "node-3", + "label": "node-3", + "data": { + "city": "110000", + "age": 21, + "amount": 100, + "wifi": "wifi-3" + } + }, + { + "id": "node-4", + "label": "node-4", + "data": { + "city": "110000", + "age": 22, + "amount": 100, + "wifi": "wifi-4" + } + }, + { + "id": "node-5", + "label": "node-5", + "data": { + "city": "310000", + "age": 30, + "amount": 1000, + "wifi": "wifi-5" + } + }, + { + "id": "node-6", + "label": "node-6", + "data": { + "city": "310000", + "age": 31, + "amount": 1000, + "wifi": "wifi-6" + } + }, + { + "id": "node-7", + "label": "node-7", + "data": { + "city": "310000", + "age": 31, + "amount": 1000, + "wifi": "wifi-7" + } + }, + { + "id": "node-8", + "label": "node-8", + "data": { + "city": "310000", + "age": 32, + "amount": 1000, + "wifi": "wifi-8" + } + }, + { + "id": "node-9", + "label": "node-9", + "data": { + "city": "310000", + "age": 30, + "amount": 1000, + "wifi": "wifi-9" + } + }, + { + "id": "node-10", + "label": "node-10", + "data": { + "city": "310000", + "age": 30, + "amount": 1000, + "wifi": "wifi-10" + } + }, + { + "id": "node-11", + "label": "node-11", + "data": { + "city": "440300", + "age": 40, + "amount": 10000, + "wifi": "wifi-11" + } + }, + { + "id": "node-12", + "label": "node-12", + "data": { + "city": "440300", + "age": 40, + "amount": 10000, + "wifi": "wifi-12" + } + }, + { + "id": "node-13", + "label": "node-13", + "data": { + "city": "440300", + "age": 41, + "amount": 10000, + "wifi": "wifi-13" + } + }, + { + "id": "node-14", + "label": "node-14", + "data": { + "city": "440300", + "age": 42, + "amount": 10000, + "wifi": "wifi-14" + } + }, + { + "id": "node-15", + "label": "node-15", + "data": { + "city": "440300", + "age": 41, + "amount": 10000, + "wifi": "wifi-15" + } + }, + { + "id": "node-16", + "label": "node-16", + "data": { + "city": "440300", + "age": 43, + "amount": 10000, + "wifi": "wifi-16" + } + } + ], + "edges": [ + { + "id": "edge-0", + "source": "node-0", + "target": "node-1", + "data": {} + }, + { + "id": "edge-1", + "source": "node-0", + "target": "node-2", + "data": {} + }, + { + "id": "edge-2", + "source": "node-0", + "target": "node-3", + "data": {} + }, + { + "id": "edge-3", + "source": "node-0", + "target": "node-4", + "data": {} + }, + { + "id": "edge-4", + "source": "node-1", + "target": "node-2", + "data": {} + }, + { + "id": "edge-5", + "source": "node-1", + "target": "node-3", + "data": {} + }, + { + "id": "edge-6", + "source": "node-1", + "target": "node-4", + "data": {} + }, + { + "id": "edge-7", + "source": "node-2", + "target": "node-3", + "data": {} + }, + { + "id": "edge-8", + "source": "node-2", + "target": "node-4", + "data": {} + }, + { + "id": "edge-9", + "source": "node-3", + "target": "node-4", + "data": {} + }, + { + "id": "edge-10", + "source": "node-5", + "target": "node-6", + "data": {} + }, + { + "id": "edge-11", + "source": "node-5", + "target": "node-7", + "data": {} + }, + { + "id": "edge-12", + "source": "node-5", + "target": "node-8", + "data": {} + }, + { + "id": "edge-13", + "source": "node-5", + "target": "node-9", + "data": {} + }, + { + "id": "edge-14", + "source": "node-6", + "target": "node-7", + "data": {} + }, + { + "id": "edge-15", + "source": "node-6", + "target": "node-8", + "data": {} + }, + { + "id": "edge-16", + "source": "node-6", + "target": "node-9", + "data": {} + }, + { + "id": "edge-17", + "source": "node-7", + "target": "node-8", + "data": {} + }, + { + "id": "edge-18", + "source": "node-7", + "target": "node-9", + "data": {} + }, + { + "id": "edge-19", + "source": "node-8", + "target": "node-9", + "data": {} + }, + { + "id": "edge-20", + "source": "node-9", + "target": "node-10", + "data": {} + }, + { + "id": "edge-40", + "source": "node-10", + "target": "node-5", + "data": {} + }, + { + "id": "edge-21", + "source": "node-11", + "target": "node-12", + "data": {} + }, + { + "id": "edge-22", + "source": "node-11", + "target": "node-13", + "data": {} + }, + { + "id": "edge-23", + "source": "node-11", + "target": "node-14", + "data": {} + }, + { + "id": "edge-24", + "source": "node-11", + "target": "node-15", + "data": {} + }, + { + "id": "edge-25", + "source": "node-12", + "target": "node-13", + "data": {} + }, + { + "id": "edge-26", + "source": "node-12", + "target": "node-14", + "data": {} + }, + { + "id": "edge-27", + "source": "node-12", + "target": "node-15", + "data": {} + }, + { + "id": "edge-28", + "source": "node-13", + "target": "node-14", + "data": {} + }, + { + "id": "edge-29", + "source": "node-13", + "target": "node-15", + "data": {} + }, + { + "id": "edge-30", + "source": "node-14", + "target": "node-15", + "data": {} + }, + { + "id": "edge-31", + "source": "node-0", + "target": "node-5", + "data": {} + }, + { + "id": "edge-32", + "source": "node-5", + "target": "node-11", + "data": {} + }, + { + "id": "edge-33", + "source": "node-11", + "target": "node-0", + "data": {} + }, + { + "id": "edge-34", + "source": "node-16", + "target": "node-0", + "data": {} + }, + { + "id": "edge-35", + "source": "node-16", + "target": "node-5", + "data": {} + }, + { + "id": "edge-36", + "source": "node-16", + "target": "node-11", + "data": {} + } + ] +} \ No newline at end of file diff --git a/packages/graph/tests/unit/find-path-spec.ts b/__tests__/unit/findPath.spec.ts similarity index 64% rename from packages/graph/tests/unit/find-path-spec.ts rename to __tests__/unit/findPath.spec.ts index e2dde9f..e6278a6 100644 --- a/packages/graph/tests/unit/find-path-spec.ts +++ b/__tests__/unit/findPath.spec.ts @@ -1,85 +1,102 @@ -import { findAllPath, findShortestPath } from '../../src'; +import { Graph } from "@antv/graphlib"; +import { findShortestPath, findAllPath } from "../../packages/graph/src"; -const data = { +const graph = new Graph({ nodes: [ { id: 'A', - label: 'A', + data: {} }, { id: 'B', - label: 'B', + data: {} }, { id: 'C', - label: 'C', + data: {} }, { id: 'D', - label: 'D', + data: {} }, { id: 'E', - label: 'E', + data: {} }, { id: 'F', - label: 'F', + data: {} }, { id: 'G', - label: 'G', + data: {} }, { id: 'H', - label: 'H', + data: {} }, ], edges: [ { + id: 'e1', source: 'A', target: 'B', + data: {} }, { + id: 'e2', source: 'B', target: 'C', + data: {} }, { + id: 'e3', source: 'C', target: 'G', + data: {} }, { + id: 'e4', source: 'A', target: 'D', + data: {} }, { + id: 'e5', source: 'A', target: 'E', + data: {} }, { + id: 'e6', source: 'E', target: 'F', + data: {} }, { + id: 'e7', source: 'F', target: 'D', + data: {} }, { + id: 'e8', source: 'D', target: 'E', + data: {} }, ], -}; +}); describe('Shortest Path from source to target on graph', () => { it('find the shortest path', () => { - const { length, path } = findShortestPath(data, 'A', 'C'); + const { length, path } = findShortestPath(graph, 'A', 'C'); expect(length).toBe(2); expect(path).toStrictEqual(['A', 'B', 'C']); }); it('find all shortest paths', () => { - const { length, allPath } = findShortestPath(data, 'A', 'F'); + const { length, allPath } = findShortestPath(graph, 'A', 'F'); expect(length).toBe(2); expect(allPath[0]).toStrictEqual(['A', 'E', 'F']); expect(allPath[1]).toStrictEqual(['A', 'D', 'F']); @@ -88,14 +105,14 @@ describe('Shortest Path from source to target on graph', () => { length: directedLenght, path: directedPath, allPath: directedAllPath, - } = findShortestPath(data, 'A', 'F', true); + } = findShortestPath(graph, 'A', 'F', true); expect(directedLenght).toBe(2); expect(directedAllPath[0]).toStrictEqual(['A', 'E', 'F']); expect(directedPath).toStrictEqual(['A', 'E', 'F']); }); it('find all paths', () => { - const allPath = findAllPath(data, 'A', 'E'); + const allPath = findAllPath(graph, 'A', 'E'); expect(allPath.length).toBe(3); expect(allPath[0]).toStrictEqual(['A', 'D', 'F', 'E']); expect(allPath[1]).toStrictEqual(['A', 'D', 'E']); @@ -103,20 +120,20 @@ describe('Shortest Path from source to target on graph', () => { }); it('find all paths in directed graph', () => { - const allPath = findAllPath(data, 'A', 'E', true); + const allPath = findAllPath(graph, 'A', 'E', true); expect(allPath.length).toStrictEqual(2); expect(allPath[0]).toStrictEqual(['A', 'D', 'E']); expect(allPath[1]).toStrictEqual(['A', 'E']); }); it('find all shortest paths in weighted graph', () => { - data.edges.forEach((edge: any, i) => { - edge.weight = ((i % 2) + 1) * 2; - if (edge.source === 'F' && edge.target === 'D') edge.weight = 10; + graph.getAllEdges().forEach((edge, i) => { + edge.data.weight = ((i % 2) + 1) * 2; + if (edge.source === 'F' && edge.target === 'D') edge.data.weight = 10; }); - const { length, path, allPath } = findShortestPath(data, 'A', 'F', false, 'weight'); + const { length, path, allPath } = findShortestPath(graph, 'A', 'F', false, 'weight'); expect(length).toBe(6); expect(allPath[0]).toStrictEqual(['A', 'E', 'F']); expect(path).toStrictEqual(['A', 'E', 'F']); }); -}); +}); \ No newline at end of file diff --git a/__tests__/unit/louvain.spec.ts b/__tests__/unit/louvain.spec.ts new file mode 100644 index 0000000..9f9185f --- /dev/null +++ b/__tests__/unit/louvain.spec.ts @@ -0,0 +1,67 @@ +import { Graph } from "@antv/graphlib"; +import { louvain, iLouvain } from "../../packages/graph/src"; +import * as propertiesGraphData from "../data/cluster-origin-properties-data.json"; + +describe('Louvain', () => { + it('simple louvain', () => { + const graph = new Graph({ + nodes: [ + { id: '0', data: {} }, { id: '1', data: {} }, { id: '2', data: {} }, { id: '3', data: {} }, { id: '4', data: {} }, + { id: '5', data: {} }, { id: '6', data: {} }, { id: '7', data: {} }, { id: '8', data: {} }, { id: '9', data: {} }, + { id: '10', data: {} }, { id: '11', data: {} }, { id: '12', data: {} }, { id: '13', data: {} }, { id: '14', data: {} }, + ], + edges: [ + { id: 'e1', source: '0', target: '1', data: {} }, { id: 'e2', source: '0', target: '2', data: {} }, { id: 'e3', source: '0', target: '3', data: {} }, { id: 'e4', source: '0', target: '4', data: {} }, + { id: 'e5', source: '1', target: '2', data: {} }, { id: 'e6', source: '1', target: '3', data: {} }, { id: 'e7', source: '1', target: '4', data: {} }, + { id: 'e8', source: '2', target: '3', data: {} }, { id: 'e9', source: '2', target: '4', data: {} }, + { id: 'e10', source: '3', target: '4', data: {} }, + { id: 'e11', source: '0', target: '0', data: {} }, + { id: 'e12', source: '0', target: '0', data: {} }, + { id: 'e13', source: '0', target: '0', data: {} }, + + { id: 'e14', source: '5', target: '6', data: {weight: 5} }, { id: 'e15', source: '5', target: '7', data: {} }, { id: 'e16', source: '5', target: '8', data: {} }, { id: 'e17', source: '5', target: '9', data: {} }, + { id: 'e18', source: '6', target: '7', data: {} }, { id: 'e19', source: '6', target: '8', data: {} }, { id: 'e20', source: '6', target: '9', data: {} }, + { id: 'e21', source: '7', target: '8', data: {} }, { id: 'e22', source: '7', target: '9', data: {} }, + { id: 'e23',source: '8', target: '9', data: {} }, + + { id: 'e24',source: '10', target: '11', data: {} }, { id: 'e25',source: '10', target: '12', data: {} }, { id: 'e26',source: '10', target: '13', data: {} }, { id: 'e27',source: '10', target: '14', data: {} }, + { id: 'e28',source: '11', target: '12', data: {} }, { id: 'e29',source: '11', target: '13', data: {} }, { id: 'e30',source: '11', target: '14', data: {} }, + { id: 'e31',source: '12', target: '13', data: {} }, { id: 'e32',source: '12', target: '14', data: {} }, + { id: 'e33',source: '13', target: '14', data: { weight: 5 } }, + + { id: 'e34',source: '0', target: '5', data: {}}, + { id: 'e35',source: '5', target: '10', data: {} }, + { id: 'e36',source: '10', target: '0', data: {} }, + { id: 'e37',source: '10', target: '0', data: {} }, + ], + }); + const clusteredData = louvain(graph, false, 'weight'); + expect(clusteredData.clusters.length).toBe(3); + expect(clusteredData.clusters[0].sumTot).toBe(3); + expect(clusteredData.clusters[1].sumTot).toBe(2); + expect(clusteredData.clusterEdges.length).toBe(6); + expect(clusteredData.clusterEdges[0].data.count).toBe(13); + expect(clusteredData.clusterEdges[1].data.count).toBe(10); + expect(clusteredData.clusterEdges[1].data.weight).toBe(14); + }); + + // it('louvain with large graph', () => { // https://gw.alipayobjects.com/os/antvdemo/assets/data/relations.json + // fetch('https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json') + // .then((res) => res.json()) + // .then((data) => { // 1589 nodes, 2747 edges + // const clusteredData = louvain(data, false, 'weight'); + // expect(clusteredData.clusters.length).toBe(495); + // expect(clusteredData.clusterEdges.length).toBe(505); + // }); + // }); + + it('louvain: add inertialModularity', () => { + const graph = new Graph(propertiesGraphData); + const clusteredData = iLouvain(graph, false, 'weight', 0.01); + expect(clusteredData.clusters.length).toBe(3); + expect(clusteredData.clusters[0].sumTot).toBe(3); + expect(clusteredData.clusters[1].sumTot).toBe(3); + expect(clusteredData.clusters[2].sumTot).toBe(4); + expect(clusteredData.clusterEdges.length).toBe(7); + }); +}); \ No newline at end of file diff --git a/__tests__/unit/pageRank.spec.ts b/__tests__/unit/pageRank.spec.ts new file mode 100644 index 0000000..ac0bfd6 --- /dev/null +++ b/__tests__/unit/pageRank.spec.ts @@ -0,0 +1,172 @@ +import { Graph } from "@antv/graphlib"; +import { pageRank } from "../../packages/graph/src"; + +describe('Calculate pagerank of graph nodes', () => { + test('calculate pagerank', () => { + const graph = new Graph({ + nodes: [ + { + id: 'A', + data: {} + }, + { + id: 'B', + data: {} + }, + { + id: 'C', + data: {} + }, + { + id: 'D', + data: {} + }, + { + id: 'E', + data: {} + }, + { + id: 'F', + data: {} + }, + { + id: 'G', + data: {} + }, + { + id: 'H', + data: {} + }, + { + id: 'I', + data: {} + }, + { + id: 'J', + data: {} + }, + { + id: 'K', + data: {} + } + ], + edges: [ + { + id: 'e0', + source: 'D', + target: 'A', + data: {} + }, + { + id: 'e1', + source: 'D', + target: 'B', + data: {} + }, + { + id: 'e2', + source: 'B', + target: 'C', + data: {} + }, + { + id: 'e3', + source: 'C', + target: 'B', + data: {} + }, + { + id: 'e4', + source: 'F', + target: 'B', + data: {} + }, + { + id: 'e5', + source: 'F', + target: 'E', + data: {} + }, + { + id: 'e6', + source: 'E', + target: 'F', + data: {} + }, + { + id: 'e7', + source: 'E', + target: 'D', + data: {} + }, + { + id: 'e8', + source: 'E', + target: 'B', + data: {} + }, + { + id: 'e9', + source: 'K', + target: 'E', + data: {} + }, + { + id: 'e10', + source: 'J', + target: 'E', + data: {} + }, + { + id: 'e11', + source: 'I', + target: 'E', + data: {} + }, + { + id: 'e12', + source: 'H', + target: 'E', + data: {} + }, + { + id: 'e13', + source: 'G', + target: 'E', + data: {} + }, + { + id: 'e14', + source: 'G', + target: 'B', + data: {} + }, + { + id: 'e15', + source: 'H', + target: 'B', + data: {} + }, + { + id: 'e16', + source: 'I', + target: 'B', + data: {} + }, + ], + }); + + const result = pageRank(graph); + + let maxNodeId; + let maxVal = 0; + for (let nodeId in result) { + const val = result[nodeId]; + if (val >= maxVal) { + maxNodeId = nodeId; + maxVal = val + } + } + expect(maxNodeId).toBe('B') + }) +}); \ No newline at end of file diff --git a/benchmarks/louvain.test.js b/benchmarks/louvain.test.js new file mode 100644 index 0000000..28ae2bf --- /dev/null +++ b/benchmarks/louvain.test.js @@ -0,0 +1,54 @@ +var Benchmark = require("benchmark"); +var suite = new Benchmark.Suite(); + +/** + * Graphology + */ +var Graph = require("graphology"); +var louvain = require('graphology-communities-louvain'); +var { louvain: AntvLouvain } = require("../packages/graph"); +var randomClusters = require("graphology-generators/random/clusters"); +var seedrandom = require("seedrandom"); +var rng = function () { + return seedrandom("bench"); +}; +const { graphology2antv } = require("./util"); + +const NODE_NUM = 10; +const EDGE_NUM = 30; +var graph = randomClusters(Graph, { + order: NODE_NUM, + size: EDGE_NUM, + clusters: 5, + rng: rng(), +}); +graph.edges().forEach(function (edge, i) { + graph.setEdgeAttribute(edge, "weight", 1); +}); +// graph.nodes().forEach(function (node) { +// graph.setNodeAttribute(node, "x", Math.random() * CANVAS_SIZE); +// graph.setNodeAttribute(node, "y", Math.random() * CANVAS_SIZE); +// }); +const antvgraph = graphology2antv(graph); + +// add tests +suite + .add("Graphology", async function () { + louvain(graph); + }) + .add("@antv/layout", async function () { + AntvLouvain(antvgraph); + }) + // add listeners + .on("cycle", function (event) { + console.log(String(event.target)); + }) + .on("complete", function () { + console.log("Fastest is " + this.filter("fastest").map("name")); + }) + .run({ async: true }); + +// logs: +// Graphology x 154,854 ops/sec ±0.38% (98 runs sampled) +// @antv/layout x 1,163 ops/sec ±0.47% (97 runs sampled) +// Fastest is Graphology diff --git a/benchmarks/util.js b/benchmarks/util.js new file mode 100644 index 0000000..c0816cf --- /dev/null +++ b/benchmarks/util.js @@ -0,0 +1,60 @@ +var { Graph } = require("@antv/graphlib"); + +const graphology2antv = (graph) => { + return new Graph({ + nodes: graph.nodes().map((id) => ({ + id, + data: { + x: graph.getNodeAttribute(id, "x"), + y: graph.getNodeAttribute(id, "y"), + }, + })), + edges: graph.edges().map((id) => ({ + id, + source: graph.source(id), + target: graph.target(id), + data: { + weight: graph.getEdgeAttribute(id, "weight"), + }, + })), + }); +}; + +const graphology2antv_wasm = (graph) => { + const nodes = []; + const masses = []; + const edges = []; + const weights = []; + const nodeIdxMap = {}; + graph.nodes().forEach((id, i) => { + nodeIdxMap[id] = i; + nodes.push( + graph.getNodeAttribute(id, "x"), + graph.getNodeAttribute(id, "y") + ); + masses.push(1); + }); + graph.edges().forEach((id) => { + const weight = graph.getEdgeAttribute(id, "weight"); + const source = nodeIdxMap[graph.source(id)]; + const target = nodeIdxMap[graph.target(id)]; + + if (source !== undefined && target !== undefined) { + // n1 <- n2 + edges.push([target, source]); + weights.push(weight); + // @see https://github.com/graphology/graphology/blob/master/src/layout-forceatlas2/helpers.js#L156-L158 + masses[source] += weight; + masses[target] += weight; + } + }); + + return { + nodes, + masses, + edges, + weights, + }; +}; + +module.exports = { graphology2antv, graphology2antv_wasm }; diff --git a/jest.config.js b/jest.config.js new file mode 100644 index 0000000..646d8b3 --- /dev/null +++ b/jest.config.js @@ -0,0 +1,30 @@ +// module.exports = { + +// preset: "ts-jest", +// collectCoverage: false, +// collectCoverageFrom: [ +// "packages/layout/src/**/*.{ts,js}", +// "!**/node_modules/**", +// "!**/vendor/**", +// ], + +/** @type {import('ts-jest').JestConfigWithTsJest} */ +module.exports = { + testEnvironment: "jsdom", + testRegex: "__tests__/.*spec\\.ts?$", + moduleDirectories: ["node_modules", "src", "es"], + moduleFileExtensions: ["js", "ts", "json"], + moduleNameMapper: { + "@antv/graph/(.*)": "/src/$1", + }, + transform: { + // '^.+\\.[tj]sx?$' to process js/ts with `ts-jest` + // '^.+\\.m?[tj]sx?$' to process js/ts/mjs/mts with `ts-jest` + "^.+\\.tsx?$": [ + "ts-jest", + { + diagnostics: false, + }, + ], + }, +}; diff --git a/package.json b/package.json index 0708270..cb0ec1a 100644 --- a/package.json +++ b/package.json @@ -1,26 +1,76 @@ { - "name": "@antv/algorithm", "private": true, + "homepage": "https://github.com/antvis/algorithm", + "bugs": { + "url": "https://github.com/antvis/algorithm/issues" + }, + "repository": { + "type": "git", + "url": "https://github.com/antvis/algorithm.git" + }, + "license": "MIT", "scripts": { + "benchmarks": "run-p benchmarks:*", + "benchmarks:louvain": "node benchmarks/louvain.test.js", "dev": "vite", + "lint:es": "eslint --ext .js --fix", + "lint:ts": "tslint -c tslint.json -p packages/graph/tsconfig.json --fix", + "lint": "npm run lint:ts && npm run lint:es", + "lint-staged": "lint-staged", "build": "pnpm -r run build", - "lint": "pnpm -r run lint", - "test": "pnpm -r run test --no-private", - "prettier": "prettier --write '**/*.{js,jsx,tsx,ts,less,md,json}'", - "pretty-quick": "pretty-quick", - "clean": "pnpm -r run clean", - "clean:modules": "rimraf node_modules", - "ls": "pnpm -r list", + "build:ci": "pnpm -r run build:ci", + "prepare": "husky install", + "test": "jest", + "coverage": "jest --coverage", "build:site": "vite build", - "deploy": "gh-pages -d site/dist" + "deploy": "gh-pages -d site/dist", + "preview": "vite preview --port 9092" + }, + "lint-staged": { + "*.js": [ + "eslint --fix" + ], + "packages/**/*/src/**/*.ts": [ + "tslint -c tslint.json -p ./tsconfig.json --fix" + ] }, "devDependencies": { "@antv/graphlib": "^2.0.0", + "@changesets/cli": "^2.26.1", + "@types/jest": "latest", + "benchmark": "^2.1.4", + "cross-env": "^7.0.3", + "eslint": "^7.32.0", + "eslint-config-airbnb-base": "^14.2.1", + "eslint-config-prettier": "^6.15.0", + "eslint-plugin-import": "^2.27.5", + "gh-pages": "^5.0.0", "graphology": "^0.25.1", + "graphology-communities-louvain": "latest", "graphology-generators": "^0.11.2", "graphology-types": "^0.24.7", "graphology-metrics": "^2.1.0", "graphology-shortest-path": "^2.0.2", - "vite": "^4.3.8" + "husky": "^7.0.4", + "jest": "^29.5.0", + "jest-environment-jsdom": "29.5.0", + "lint-staged": "^10.5.4", + "npm-run-all": "^4.1.5", + "rimraf": "^3.0.2", + "seedrandom": "^3.0.5", + "ts-jest": "^29.1.0", + "ts-loader": "^8.4.0", + "tslint": "^6.1.3", + "tslint-config-airbnb": "^5.11.2", + "tslint-config-prettier": "^1.18.0", + "tslint-eslint-rules": "^5.4.0", + "typescript": "^4.9.5", + "vite": "^4.3.8", + "webpack": "^5.38.1", + "webpack-cli": "^5.0.2" + }, + "publishConfig": { + "access": "public", + "registry": "https://registry.npmjs.org" } } diff --git a/packages/graph-gpu/.prettierrc.js b/packages/graph-gpu/.prettierrc.js deleted file mode 100644 index 7b597d7..0000000 --- a/packages/graph-gpu/.prettierrc.js +++ /dev/null @@ -1,5 +0,0 @@ -const fabric = require('@umijs/fabric'); - -module.exports = { - ...fabric.prettier, -}; diff --git a/packages/graph-gpu/package.json b/packages/graph-gpu/package.json index 8eb5c94..177e9a3 100644 --- a/packages/graph-gpu/package.json +++ b/packages/graph-gpu/package.json @@ -31,15 +31,10 @@ "dev:umd": "webpack --config webpack.dev.config.js --mode development", "ci": "npm run build && npm run coverage", "clean": "rimraf lib dist", - "coverage": "jest --coverage", "lint": "eslint --ext .js,.jsx,.ts,.tsx --format=pretty \"./\"", "lint:src": "eslint --ext .ts --format=pretty \"./src\"", "prettier": "prettier -c --write \"**/*\"", - "test": "npm run build:umd && jest", - "test-live": "npm run build:umd && DEBUG_MODE=1 jest --watch ./tests/unit/louvain-spec.ts", - "test-live:async": "npm run build:umd && DEBUG_MODE=1 jest --watch ./tests/unit/louvain-async-spec.ts", - "lint-staged:js": "eslint --ext .js,.jsx,.ts,.tsx", - "cdn": "antv-bin upload -n @antv/webgpu-graph" + "lint-staged:js": "eslint --ext .js,.jsx,.ts,.tsx" }, "homepage": "https://g6.antv.vision", "bugs": { @@ -56,17 +51,7 @@ "@babel/plugin-proposal-class-properties": "^7.12.1", "@babel/preset-env": "^7.12.7", "@babel/preset-typescript": "^7.12.7", - "@types/jest": "^26.0.18", - "@umijs/fabric": "^2.5.6", - "babel-loader": "^8.2.2", - "jest": "^26.6.3", - "rimraf": "^3.0.2", - "ts-jest": "^26.4.4", - "ts-loader": "^8.0.14", - "tslint": "^6.1.3", - "typescript": "^4.1.3", - "webpack": "^5.38.1", - "webpack-cli": "^5.0.2" + "babel-loader": "^8.2.2" }, "dependencies": { "@antv/g": "^5.16.26", diff --git a/packages/graph-gpu/src/WebGPUGraph.ts b/packages/graph-gpu/src/WebGPUGraph.ts index a75208f..d1b1c2d 100644 --- a/packages/graph-gpu/src/WebGPUGraph.ts +++ b/packages/graph-gpu/src/WebGPUGraph.ts @@ -1,7 +1,7 @@ import { Canvas } from '@antv/g'; import { Renderer } from '@antv/g-webgpu'; import { Plugin } from '@antv/g-plugin-gpgpu'; -import { pageRank } from './link-analysis'; +import { PageRankParams, pageRank } from './link-analysis'; import { sssp } from './traversal'; import { Graph } from './types'; @@ -41,9 +41,9 @@ export class WebGPUGraph { return plugin.getDevice(); } - async pageRank(graphData: Graph, eps = 1e-5, alpha = 0.85, maxIteration = 1000) { + async pageRank(graphData: Graph, params?: PageRankParams) { const device = await this.getDevice(); - return pageRank(device, graphData, eps, alpha, maxIteration); + return pageRank(device, graphData, params); } async sssp(graphData: Graph, sourceId: string, weightPropertyName: string = 'weight') { diff --git a/packages/graph-gpu/src/index.ts b/packages/graph-gpu/src/index.ts index 70508e2..fb741ab 100644 --- a/packages/graph-gpu/src/index.ts +++ b/packages/graph-gpu/src/index.ts @@ -1,3 +1,4 @@ export * from './WebGPUGraph'; export * from './link-analysis'; export * from './traversal'; +export * from './types'; diff --git a/packages/graph-gpu/src/link-analysis/pageRank.ts b/packages/graph-gpu/src/link-analysis/pageRank.ts index ba2eeb5..d948145 100644 --- a/packages/graph-gpu/src/link-analysis/pageRank.ts +++ b/packages/graph-gpu/src/link-analysis/pageRank.ts @@ -1,10 +1,16 @@ -import { DeviceRenderer } from '@antv/g-webgpu'; -import { Kernel } from '@antv/g-plugin-gpgpu'; -import { convertGraphData2CSC } from '../util'; -import { Graph } from '../types'; +import { DeviceRenderer } from "@antv/g-webgpu"; +import { Kernel } from "@antv/g-plugin-gpgpu"; +import { convertGraphData2CSC } from "../util"; +import { Graph } from "../types"; const { BufferUsage } = DeviceRenderer; +export interface PageRankParams { + alpha: number; + maxIterations: number; + tolerance: number; +} + /** * Pagerank using power method, ported from CUDA * @@ -18,10 +24,14 @@ const { BufferUsage } = DeviceRenderer; export async function pageRank( device: DeviceRenderer.Device, graphData: Graph, - eps = 1e-5, - alpha = 0.85, - maxIteration = 1000, + params?: PageRankParams ) { + let { + tolerance = 1e-5, + alpha = 0.85, + maxIterations = 1000 + } = params || {}; + const BLOCK_SIZE = 1; const BLOCKS = 256; @@ -145,14 +155,14 @@ fn main( const grids = Math.ceil(V.length / (BLOCKS * BLOCK_SIZE)); - while (maxIteration--) { + while (maxIterations--) { storeKernel.dispatch(grids, 1); matmulKernel.dispatch(grids, 1); rankDiffKernel.dispatch(grids, 1); const last = (await readback.readBuffer(rLastBuffer)) as Float32Array; const result = last.reduce((prev, cur) => prev + cur, 0); - if (result < eps) { + if (result < tolerance) { break; } } diff --git a/packages/graph-gpu/src/traversal/sssp.ts b/packages/graph-gpu/src/traversal/sssp.ts index 82638ae..cb20ee6 100644 --- a/packages/graph-gpu/src/traversal/sssp.ts +++ b/packages/graph-gpu/src/traversal/sssp.ts @@ -196,7 +196,7 @@ fn main( updatePredKernel.dispatch(grids, 1); const out = (await readback.readBuffer(DiOutBuffer)) as Float32Array; - const predecessor = await readback.readBuffer(POutBuffer); + const predecessor = await readback.readBuffer(POutBuffer) as Float32Array; return Array.from(out).map((distance, i) => ({ target: graphData.getAllNodes()[V[i]].id, diff --git a/packages/graph-gpu/tsconfig.json b/packages/graph-gpu/tsconfig.json index ba439d3..2ccebef 100644 --- a/packages/graph-gpu/tsconfig.json +++ b/packages/graph-gpu/tsconfig.json @@ -1,4 +1,5 @@ { + "extends": "../../tsconfig.json", "compilerOptions": { "rootDir": "./src", "composite": true, diff --git a/packages/graph-rust/Cargo.toml b/packages/graph-rust/Cargo.toml index 7a4b3d4..948423c 100644 --- a/packages/graph-rust/Cargo.toml +++ b/packages/graph-rust/Cargo.toml @@ -17,7 +17,44 @@ lto = true opt-level = 's' [dependencies] -graph_builder = "0.3.1" +atoi = "2.0.0" +atomic = "0.5.1" +atomic_float = "0.1.0" +byte-slice-cast = "1.2.2" +dashmap = "5.4.0" +delegate = "0.8.0" +fast-float = "0.2.0" +fxhash = "0.2.1" +gdl = { version = "0.2.7", optional = true} rayon = "1.5.1" +serde = { optional = true } +slotmap = {version = "0.4"} + +[dependencies.linereader] +version = "0.4.0" + +[dependencies.log] +version = "0.4.17" + +[dependencies.memmap2] +version = "0.5.8" + +[dependencies.num] +version = "0.4.0" + +[dependencies.num-format] +version = "0.4.3" + +[dependencies.num_cpus] +version = "1.14.0" + +[dependencies.page_size] +version = "0.4.2" + +[dependencies.parking_lot] +version = "0.12.1" + +[dependencies.thiserror] +version = "1.0.37" diff --git a/packages/graph-rust/README.md b/packages/graph-rust/README.md new file mode 100644 index 0000000..e2794fb --- /dev/null +++ b/packages/graph-rust/README.md @@ -0,0 +1,3 @@ +# @antv/graph-rust + +This crate builds on top of the [graph_builder](https://docs.rs/graph_builder/latest/graph_builder/) crate, which can be used as a building block for custom graph algorithms. \ No newline at end of file diff --git a/packages/graph-rust/rustfmt.toml b/packages/graph-rust/rustfmt.toml new file mode 100644 index 0000000..6df7c47 --- /dev/null +++ b/packages/graph-rust/rustfmt.toml @@ -0,0 +1,8 @@ +hard_tabs = false +newline_style = "unix" + +unstable_features = true +format_code_in_doc_comments = true +format_macro_bodies = true +format_macro_matchers = true +format_strings = true diff --git a/packages/graph-rust/src/graph_builder/builder.rs b/packages/graph-rust/src/graph_builder/builder.rs new file mode 100644 index 0000000..46be7cd --- /dev/null +++ b/packages/graph-rust/src/graph_builder/builder.rs @@ -0,0 +1,341 @@ +use std::{convert::TryFrom, marker::PhantomData}; + +use super::{ + graph::csr::{CsrLayout, NodeValues}, + index::Idx, + input::{edgelist::EdgeList, InputCapabilities, InputPath}, + prelude::edgelist::{EdgeIterator, EdgeWithValueIterator}, + Error, +}; +use std::path::Path as StdPath; + +pub struct Uninitialized { + csr_layout: CsrLayout, +} + +pub struct FromEdges +where + NI: Idx, + Edges: IntoIterator, +{ + csr_layout: CsrLayout, + edges: Edges, + _node: PhantomData, +} + +pub struct FromEdgeListAndNodeValues +where + NI: Idx, +{ + csr_layout: CsrLayout, + node_values: NodeValues, + edge_list: EdgeList, +} + +pub struct FromEdgesWithValues +where + NI: Idx, + Edges: IntoIterator, +{ + csr_layout: CsrLayout, + edges: Edges, + _node: PhantomData, +} + +pub struct FromInput +where + P: AsRef, + NI: Idx, + Format: InputCapabilities, + Format::GraphInput: TryFrom>, +{ + csr_layout: CsrLayout, + _idx: PhantomData, + _path: PhantomData

, + _format: PhantomData, +} + +pub struct FromPath +where + P: AsRef, + NI: Idx, + Format: InputCapabilities, + Format::GraphInput: TryFrom>, +{ + csr_layout: CsrLayout, + path: P, + _idx: PhantomData, + _format: PhantomData, +} + +/// A builder to create graphs in a type-safe way. +/// +/// The builder implementation uses different states to allow staged building of +/// graphs. Each individual state enables stage-specific methods on the builder. +/// +/// # Examples +/// +/// Create a directed graph from a vec of edges: +/// +/// ``` +/// use graph_builder::prelude::*; +/// +/// let graph: DirectedCsrGraph = GraphBuilder::new() +/// .edges(vec![(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]) +/// .build(); +/// +/// assert_eq!(graph.node_count(), 4); +/// ``` +/// +/// Create an undirected graph from a vec of edges: +/// +/// ``` +/// use graph_builder::prelude::*; +/// +/// let graph: UndirectedCsrGraph = GraphBuilder::new() +/// .edges(vec![(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]) +/// .build(); +/// +/// assert_eq!(graph.node_count(), 4); +/// ``` +pub struct GraphBuilder { + state: State, +} + +impl Default for GraphBuilder { + fn default() -> Self { + GraphBuilder::new() + } +} + +impl GraphBuilder { + /// Creates a new builder + pub fn new() -> Self { + Self { + state: Uninitialized { + csr_layout: CsrLayout::default(), + }, + } + } + + /// Sets the [`CsrLayout`] to use during CSR construction. + /// + /// # Examples + /// + /// Store the neighbors sorted: + /// + /// ``` + /// use graph_builder::prelude::*; + /// + /// let graph: UndirectedCsrGraph = GraphBuilder::new() + /// .csr_layout(CsrLayout::Sorted) + /// .edges(vec![(0, 7), (0, 3), (0, 3), (0, 1)]) + /// .build(); + /// + /// assert_eq!(graph.neighbors(0).copied().collect::>(), &[1, 3, 3, 7]); + /// ``` + /// + /// Store the neighbors sorted and deduplicated: + /// + /// ``` + /// use graph_builder::prelude::*; + /// + /// let graph: UndirectedCsrGraph = GraphBuilder::new() + /// .csr_layout(CsrLayout::Deduplicated) + /// .edges(vec![(0, 7), (0, 3), (0, 3), (0, 1)]) + /// .build(); + /// + /// assert_eq!(graph.neighbors(0).copied().collect::>(), &[1, 3, 7]); + /// ``` + #[must_use] + pub fn csr_layout(mut self, csr_layout: CsrLayout) -> Self { + self.state.csr_layout = csr_layout; + self + } + + /// Create a graph from the given edge tuples. + /// + /// # Example + /// + /// ``` + /// use graph_builder::prelude::*; + /// + /// let graph: DirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]) + /// .build(); + /// + /// assert_eq!(graph.node_count(), 4); + /// assert_eq!(graph.edge_count(), 5); + /// ``` + pub fn edges(self, edges: Edges) -> GraphBuilder> + where + NI: Idx, + Edges: IntoIterator, + { + GraphBuilder { + state: FromEdges { + csr_layout: self.state.csr_layout, + edges, + _node: PhantomData, + }, + } + } + + /// Create a graph from the given edge triplets. + /// + /// # Example + /// + /// ``` + /// use graph_builder::prelude::*; + /// + /// let graph: DirectedCsrGraph = GraphBuilder::new() + /// .edges_with_values(vec![(0, 1, 0.1), (0, 2, 0.2), (1, 2, 0.3), (1, 3, 0.4), (2, 3, 0.5)]) + /// .build(); + /// + /// assert_eq!(graph.node_count(), 4); + /// assert_eq!(graph.edge_count(), 5); + /// ``` + pub fn edges_with_values( + self, + edges: Edges, + ) -> GraphBuilder> + where + NI: Idx, + Edges: IntoIterator, + { + GraphBuilder { + state: FromEdgesWithValues { + csr_layout: self.state.csr_layout, + edges, + _node: PhantomData, + }, + } + } +} + +impl GraphBuilder> +where + NI: Idx, + Edges: IntoIterator, +{ + pub fn node_values( + self, + node_values: I, + ) -> GraphBuilder> + where + I: IntoIterator, + { + let edge_list = EdgeList::from(EdgeIterator(self.state.edges)); + let node_values = node_values.into_iter().collect::>(); + + GraphBuilder { + state: FromEdgeListAndNodeValues { + csr_layout: self.state.csr_layout, + node_values, + edge_list, + }, + } + } + + /// Build the graph from the given vec of edges. + pub fn build(self) -> Graph + where + Graph: From<(EdgeList, CsrLayout)>, + { + Graph::from(( + EdgeList::from(EdgeIterator(self.state.edges)), + self.state.csr_layout, + )) + } +} + +impl GraphBuilder> +where + NI: Idx, + EV: Sync, + Edges: IntoIterator, +{ + pub fn node_values( + self, + node_values: I, + ) -> GraphBuilder> + where + I: IntoIterator, + { + let edge_list = EdgeList::from(EdgeWithValueIterator(self.state.edges)); + let node_values = node_values.into_iter().collect::>(); + + GraphBuilder { + state: FromEdgeListAndNodeValues { + csr_layout: self.state.csr_layout, + node_values, + edge_list, + }, + } + } + + /// Build the graph from the given vec of edges. + pub fn build(self) -> Graph + where + Graph: From<(EdgeList, CsrLayout)>, + { + Graph::from(( + EdgeList::new(self.state.edges.into_iter().collect()), + self.state.csr_layout, + )) + } +} + +impl GraphBuilder> { + pub fn build(self) -> Graph + where + Graph: From<(NodeValues, EdgeList, CsrLayout)>, + { + Graph::from(( + self.state.node_values, + self.state.edge_list, + self.state.csr_layout, + )) + } +} + +impl GraphBuilder> +where + Path: AsRef, + NI: Idx, + Format: InputCapabilities, + Format::GraphInput: TryFrom>, +{ + /// Set the location where the graph is stored. + pub fn path(self, path: Path) -> GraphBuilder> { + GraphBuilder { + state: FromPath { + csr_layout: self.state.csr_layout, + path, + _idx: PhantomData, + _format: PhantomData, + }, + } + } +} + +impl GraphBuilder> +where + Path: AsRef, + NI: Idx, + Format: InputCapabilities, + Format::GraphInput: TryFrom>, + super::Error: From<>>::Error>, +{ + /// Build the graph from the given input format and path. + pub fn build(self) -> Result + where + Graph: TryFrom<(Format::GraphInput, CsrLayout)>, + super::Error: From, + { + let input = Format::GraphInput::try_from(InputPath(self.state.path))?; + let graph = Graph::try_from((input, self.state.csr_layout))?; + + Ok(graph) + } +} \ No newline at end of file diff --git a/packages/graph-rust/src/graph_builder/compat.rs b/packages/graph-rust/src/graph_builder/compat.rs new file mode 100644 index 0000000..5dcdf25 --- /dev/null +++ b/packages/graph-rust/src/graph_builder/compat.rs @@ -0,0 +1,109 @@ +use std::mem::MaybeUninit; + +pub(crate) trait MaybeUninitWriteSliceExt { + fn write_slice_compat<'a>(this: &'a mut [MaybeUninit], src: &[T]) -> &'a mut [T] + where + T: Copy; +} + +#[cfg(not(has_maybe_uninit_write_slice))] +impl MaybeUninitWriteSliceExt for MaybeUninit { + fn write_slice_compat<'a>(this: &'a mut [MaybeUninit], src: &[T]) -> &'a mut [T] + where + T: Copy, + { + // SAFETY: &[T] and &[MaybeUninit] have the same layout + let uninit_src: &[MaybeUninit] = unsafe { std::mem::transmute(src) }; + + this.copy_from_slice(uninit_src); + + // SAFETY: Valid elements have just been copied into `this` so it is initialized + // SAFETY: similar to safety notes for `slice_get_ref`, but we have a + // mutable reference which is also guaranteed to be valid for writes. + unsafe { &mut *(this as *mut [MaybeUninit] as *mut [T]) } + } +} + +#[cfg(has_maybe_uninit_write_slice)] +impl MaybeUninitWriteSliceExt for MaybeUninit { + fn write_slice_compat<'a>(this: &'a mut [MaybeUninit], src: &[T]) -> &'a mut [T] + where + T: Copy, + { + MaybeUninit::write_slice(this, src) + } +} + +pub(crate) trait NewUninitExt { + fn new_uninit_slice_compat(len: usize) -> Box<[MaybeUninit]>; + + unsafe fn assume_init_compat(self) -> Box<[T]>; +} + +#[cfg(not(has_new_uninit))] +impl NewUninitExt for Box<[MaybeUninit]> { + fn new_uninit_slice_compat(len: usize) -> Box<[MaybeUninit]> { + use std::mem::ManuallyDrop; + use std::slice::from_raw_parts_mut; + + let vec = Vec::::with_capacity(len); + let mut vec = ManuallyDrop::new(vec); + + unsafe { + let slice = from_raw_parts_mut(vec.as_mut_ptr() as *mut MaybeUninit, len); + Box::from_raw(slice) + } + } + + unsafe fn assume_init_compat(self) -> Box<[T]> { + unsafe { Box::from_raw(Box::into_raw(self) as *mut [T]) } + } +} + +#[cfg(has_new_uninit)] +impl NewUninitExt for Box<[MaybeUninit]> { + fn new_uninit_slice_compat(len: usize) -> Box<[MaybeUninit]> { + Box::<[T]>::new_uninit_slice(len) + } + + unsafe fn assume_init_compat(self) -> Box<[T]> { + unsafe { self.assume_init() } + } +} + +pub(crate) trait SlicePartitionDedupExt { + fn partition_dedup_compat(&mut self) -> (&mut [T], &mut [T]); +} + +#[cfg(not(has_slice_partition_dedup))] +impl SlicePartitionDedupExt for [T] { + fn partition_dedup_compat(&mut self) -> (&mut [T], &mut [T]) { + let len = self.len(); + if len <= 1 { + return (self, &mut []); + } + + let ptr = self.as_mut_ptr(); + let mut next_read: usize = 1; + let mut next_write: usize = 1; + + unsafe { + while next_read < len { + let ptr_read = ptr.add(next_read); + let prev_ptr_write = ptr.add(next_write - 1); + // Changed from if `!same_bucket(&mut *ptr_read, &mut *prev_ptr_write)` + // as the original implementation is copied from `partition_dedup_by`. + if *ptr_read != *prev_ptr_write { + if next_read != next_write { + let ptr_write = prev_ptr_write.offset(1); + core::ptr::swap(ptr_read, ptr_write); + } + next_write += 1; + } + next_read += 1; + } + } + + self.split_at_mut(next_write) + } +} diff --git a/packages/graph-rust/src/graph_builder/graph/csr.rs b/packages/graph-rust/src/graph_builder/graph/csr.rs new file mode 100644 index 0000000..ee194cd --- /dev/null +++ b/packages/graph-rust/src/graph_builder/graph/csr.rs @@ -0,0 +1,873 @@ +use atomic::Atomic; +use byte_slice_cast::{AsByteSlice, AsMutByteSlice, ToByteSlice, ToMutByteSlice}; +use std::{ + convert::TryFrom, + fs::File, + io::{BufReader, Read, Write}, + iter::FromIterator, + mem::{ManuallyDrop, MaybeUninit}, + path::PathBuf, + sync::atomic::Ordering::Acquire, +}; + +use rayon::prelude::*; + +use super::super::compat::*; +use super::super::{ + graph_ops::{DeserializeGraphOp, SerializeGraphOp, ToUndirectedOp}, + index::Idx, + input::{edgelist::Edges, Direction}, + DirectedDegrees, DirectedNeighbors, DirectedNeighborsWithValues, Error, Graph, + NodeValues as NodeValuesTrait, SharedMut, UndirectedDegrees, UndirectedNeighbors, + UndirectedNeighborsWithValues, +}; + +/// Defines how the neighbor list of individual nodes are organized within the +/// CSR target array. +#[derive(Clone, Copy, Debug)] +pub enum CsrLayout { + /// Neighbor lists are sorted and may contain duplicate target ids. This is + /// the default representation. + Sorted, + /// Neighbor lists are not in any particular order. + Unsorted, + /// Neighbor lists are sorted and do not contain duplicate target ids. + /// Self-loops, i.e., edges in the form of `(u, u)` are removed. + Deduplicated, +} + +impl Default for CsrLayout { + fn default() -> Self { + CsrLayout::Unsorted + } +} + +/// A Compressed-Sparse-Row data structure to represent sparse graphs. +/// +/// The data structure is composed of two arrays: `offsets` and `targets`. For a +/// graph with node count `n` and edge count `m`, `offsets` has exactly `n + 1` +/// and `targets` exactly `m` entries. +/// +/// For a given node `u`, `offsets[u]` stores the start index of the neighbor +/// list of `u` in `targets`. The degree of `u`, i.e., the length of the +/// neighbor list is defined by `offsets[u + 1] - offsets[u]`. The neighbor list +/// of `u` is defined by the slice `&targets[offsets[u]..offsets[u + 1]]`. +#[derive(Debug)] +pub struct Csr { + offsets: Box<[Index]>, + targets: Box<[Target]>, +} + +/// Represents the target of an edge and its associated value. +#[derive(Clone, Copy, Debug)] +#[repr(C)] +pub struct Target { + pub target: NI, + pub value: EV, +} + +impl Ord for Target { + fn cmp(&self, other: &Self) -> std::cmp::Ordering { + self.target.cmp(&other.target) + } +} + +impl PartialOrd for Target { + fn partial_cmp(&self, other: &Self) -> Option { + self.target.partial_cmp(&other.target) + } +} + +impl PartialEq for Target { + fn eq(&self, other: &Self) -> bool { + self.target.eq(&other.target) + } +} + +impl Eq for Target {} + +impl Target { + pub fn new(target: T, value: EV) -> Self { + Self { target, value } + } +} + +impl Csr { + pub(crate) fn new(offsets: Box<[Index]>, targets: Box<[Target]>) -> Self { + Self { offsets, targets } + } + + #[inline] + pub(crate) fn node_count(&self) -> Index { + Index::new(self.offsets.len() - 1) + } + + #[inline] + pub(crate) fn edge_count(&self) -> Index { + Index::new(self.targets.len()) + } + + #[inline] + pub(crate) fn degree(&self, i: Index) -> Index { + let from = self.offsets[i.index()]; + let to = self.offsets[(i + Index::new(1)).index()]; + + to - from + } + + #[inline] + pub(crate) fn targets_with_values(&self, i: Index) -> &[Target] { + let from = self.offsets[i.index()]; + let to = self.offsets[(i + Index::new(1)).index()]; + + &self.targets[from.index()..to.index()] + } +} + +impl Csr { + #[inline] + pub(crate) fn targets(&self, i: Index) -> &[NI] { + assert_eq!( + std::mem::size_of::>(), + std::mem::size_of::() + ); + assert_eq!( + std::mem::align_of::>(), + std::mem::align_of::() + ); + let from = self.offsets[i.index()]; + let to = self.offsets[(i + Index::new(1)).index()]; + + let len = (to - from).index(); + + let targets = &self.targets[from.index()..to.index()]; + + // SAFETY: len is within bounds as it is calculated above as `to - from`. + // The types Target and T are verified to have the same + // size and alignment. + unsafe { std::slice::from_raw_parts(targets.as_ptr() as *const _, len) } + } +} + +pub trait SwapCsr { + fn swap_csr(&mut self, csr: Csr) -> &mut Self; +} + +impl From<(&'_ E, NI, Direction, CsrLayout)> for Csr +where + NI: Idx, + EV: Copy + Send + Sync, + E: Edges, +{ + fn from( + (edge_list, node_count, direction, csr_layout): (&'_ E, NI, Direction, CsrLayout), + ) -> Self { + let degrees = edge_list.degrees(node_count, direction); + let offsets = prefix_sum_atomic(degrees); + let edge_count = offsets[node_count.index()].load(Acquire).index(); + let mut targets = Vec::>::with_capacity(edge_count); + let targets_ptr = SharedMut::new(targets.as_mut_ptr()); + + // The following loop writes all targets into their correct position. + // The offsets are a prefix sum of all degrees, which will produce + // non-overlapping positions for all node values. + // + // SAFETY: for any (s, t) tuple from the same edge_list we use the + // prefix_sum to find a unique position for the target value, so that we + // only write once into each position and every thread that might run + // will write into different positions. + if matches!(direction, Direction::Outgoing | Direction::Undirected) { + edge_list.edges().for_each(|(s, t, v)| { + let offset = NI::get_and_increment(&offsets[s.index()], Acquire); + + unsafe { + targets_ptr.add(offset.index()).write(Target::new(t, v)); + } + }) + } + + if matches!(direction, Direction::Incoming | Direction::Undirected) { + edge_list.edges().for_each(|(s, t, v)| { + let offset = NI::get_and_increment(&offsets[t.index()], Acquire); + + unsafe { + targets_ptr.add(offset.index()).write(Target::new(s, v)); + } + }) + } + + // SAFETY: The previous loops iterated the input edge list once (twice + // for undirected) and inserted one node id for each edge. The + // `edge_count` is defined by the highest offset value. + unsafe { + targets.set_len(edge_count); + } + let mut offsets = ManuallyDrop::new(offsets); + let (ptr, len, cap) = (offsets.as_mut_ptr(), offsets.len(), offsets.capacity()); + + // SAFETY: NI and NI::Atomic have the same memory layout + let mut offsets = unsafe { + let ptr = ptr as *mut _; + Vec::from_raw_parts(ptr, len, cap) + }; + + // Each insert into the target array in the previous loops incremented + // the offset for the corresponding node by one. As a consequence the + // offset values are shifted one index to the right. We need to correct + // this in order to get correct offsets. + offsets.rotate_right(1); + offsets[0] = NI::zero(); + + let (offsets, targets) = match csr_layout { + CsrLayout::Unsorted => (offsets, targets), + CsrLayout::Sorted => { + sort_targets(&offsets, &mut targets); + (offsets, targets) + } + CsrLayout::Deduplicated => { + let offsets_targets = sort_and_deduplicate_targets(&offsets, &mut targets[..]); + offsets_targets + } + }; + + Csr { + offsets: offsets.into_boxed_slice(), + targets: targets.into_boxed_slice(), + } + } +} + +unsafe impl ToByteSlice for Target +where + NI: ToByteSlice, + EV: ToByteSlice, +{ + fn to_byte_slice + ?Sized>(slice: &S) -> &[u8] { + let slice = slice.as_ref(); + let len = slice.len() * std::mem::size_of::>(); + unsafe { std::slice::from_raw_parts(slice.as_ptr() as *const u8, len) } + } +} + +unsafe impl ToMutByteSlice for Target +where + NI: ToMutByteSlice, + EV: ToMutByteSlice, +{ + fn to_mut_byte_slice + ?Sized>(slice: &mut S) -> &mut [u8] { + let slice = slice.as_mut(); + let len = slice.len() * std::mem::size_of::>(); + unsafe { std::slice::from_raw_parts_mut(slice.as_mut_ptr() as *mut u8, len) } + } +} + +impl Csr +where + NI: Idx + ToByteSlice, + EV: ToByteSlice, +{ + fn serialize(&self, output: &mut W) -> Result<(), Error> { + let type_name = std::any::type_name::().as_bytes(); + output.write_all([type_name.len()].as_byte_slice())?; + output.write_all(type_name)?; + + let node_count = self.node_count(); + let edge_count = self.edge_count(); + let meta = [node_count, edge_count]; + output.write_all(meta.as_byte_slice())?; + + output.write_all(self.offsets.as_byte_slice())?; + output.write_all(self.targets.as_byte_slice())?; + + Ok(()) + } +} + +impl Csr +where + NI: Idx + ToMutByteSlice, + EV: ToMutByteSlice, +{ + fn deserialize(read: &mut R) -> Result, Error> { + let mut type_name_len = [0_usize; 1]; + read.read_exact(type_name_len.as_mut_byte_slice())?; + let [type_name_len] = type_name_len; + + let mut type_name = vec![0_u8; type_name_len]; + read.read_exact(type_name.as_mut_byte_slice())?; + let type_name = String::from_utf8(type_name).expect("could not read type name"); + + let expected_type_name = std::any::type_name::().to_string(); + + if type_name != expected_type_name { + return Err(Error::InvalidIdType { + expected: expected_type_name, + actual: type_name, + }); + } + + let mut meta = [NI::zero(); 2]; + read.read_exact(meta.as_mut_byte_slice())?; + + let [node_count, edge_count] = meta; + + let mut offsets = Box::new_uninit_slice_compat(node_count.index() + 1); + let offsets_ptr = offsets.as_mut_ptr() as *mut NI; + let offsets_ptr = + unsafe { std::slice::from_raw_parts_mut(offsets_ptr, node_count.index() + 1) }; + read.read_exact(offsets_ptr.as_mut_byte_slice())?; + + let mut targets = Box::new_uninit_slice_compat(edge_count.index()); + let targets_ptr = targets.as_mut_ptr() as *mut Target; + let targets_ptr = + unsafe { std::slice::from_raw_parts_mut(targets_ptr, edge_count.index()) }; + read.read_exact(targets_ptr.as_mut_byte_slice())?; + + let offsets = unsafe { offsets.assume_init_compat() }; + let targets = unsafe { targets.assume_init_compat() }; + + Ok(Csr::new(offsets, targets)) + } +} + +pub struct NodeValues(Box<[NV]>); + +impl NodeValues { + pub fn new(node_values: Vec) -> Self { + Self(node_values.into_boxed_slice()) + } +} + +impl FromIterator for NodeValues { + fn from_iter>(iter: T) -> Self { + Self(iter.into_iter().collect::>().into_boxed_slice()) + } +} + +impl NodeValues +where + NV: ToByteSlice, +{ + fn serialize(&self, output: &mut W) -> Result<(), Error> { + let node_count = self.0.len(); + let meta = [node_count]; + output.write_all(meta.as_byte_slice())?; + output.write_all(self.0.as_byte_slice())?; + Ok(()) + } +} + +impl NodeValues +where + NV: ToMutByteSlice, +{ + fn deserialize(read: &mut R) -> Result { + let mut meta = [0_usize; 1]; + read.read_exact(meta.as_mut_byte_slice())?; + let [node_count] = meta; + + let mut node_values = Box::new_uninit_slice_compat(node_count); + let node_values_ptr = node_values.as_mut_ptr() as *mut NV; + let node_values_slice = + unsafe { std::slice::from_raw_parts_mut(node_values_ptr, node_count.index()) }; + read.read_exact(node_values_slice.as_mut_byte_slice())?; + + let offsets = unsafe { node_values.assume_init_compat() }; + + Ok(NodeValues(offsets)) + } +} + +pub struct DirectedCsrGraph { + node_values: NodeValues, + csr_out: Csr, + csr_inc: Csr, +} + +impl DirectedCsrGraph { + pub fn new( + node_values: NodeValues, + csr_out: Csr, + csr_inc: Csr, + ) -> Self { + let g = Self { + node_values, + csr_out, + csr_inc, + }; + + g + } +} + +impl ToUndirectedOp for DirectedCsrGraph +where + NI: Idx, + NV: Clone + Send + Sync, + EV: Copy + Send + Sync, +{ + type Undirected = UndirectedCsrGraph; + + fn to_undirected(&self, layout: impl Into>) -> Self::Undirected { + let node_values = NodeValues::new(self.node_values.0.to_vec()); + let layout = layout.into().unwrap_or_default(); + let edges = ToUndirectedEdges { g: self }; + + UndirectedCsrGraph::from((node_values, edges, layout)) + } +} + +struct ToUndirectedEdges<'g, NI: Idx, NV, EV> { + g: &'g DirectedCsrGraph, +} + +impl<'g, NI, NV, EV> Edges for ToUndirectedEdges<'g, NI, NV, EV> +where + NI: Idx, + NV: Send + Sync, + EV: Copy + Send + Sync, +{ + type NI = NI; + + type EV = EV; + + type EdgeIter<'a> = ToUndirectedEdgesIter<'a, NI, NV, EV> + where + Self: 'a; + + fn edges(&self) -> Self::EdgeIter<'_> { + ToUndirectedEdgesIter { g: self.g } + } + + fn max_node_id(&self) -> Self::NI { + self.g.node_count() - NI::new(1) + } + + #[cfg(test)] + fn len(&self) -> usize { + unimplemented!("This type is not used in tests") + } +} + +struct ToUndirectedEdgesIter<'g, NI: Idx, NV, EV> { + g: &'g DirectedCsrGraph, +} + +impl<'g, NI: Idx, NV: Send + Sync, EV: Copy + Send + Sync> ParallelIterator + for ToUndirectedEdgesIter<'g, NI, NV, EV> +{ + type Item = (NI, NI, EV); + + fn drive_unindexed(self, consumer: C) -> C::Result + where + C: rayon::iter::plumbing::UnindexedConsumer, + { + let par_iter = (0..self.g.node_count().index()) + .into_par_iter() + .flat_map_iter(|n| { + let n = NI::new(n); + self.g + .out_neighbors_with_values(n) + .map(move |t| (n, t.target, t.value)) + }); + par_iter.drive_unindexed(consumer) + } +} + +impl Graph for DirectedCsrGraph { + delegate::delegate! { + to self.csr_out { + fn node_count(&self) -> NI; + fn edge_count(&self) -> NI; + } + } +} + +impl NodeValuesTrait for DirectedCsrGraph { + fn node_value(&self, node: NI) -> &NV { + &self.node_values.0[node.index()] + } +} + +impl DirectedDegrees for DirectedCsrGraph { + fn out_degree(&self, node: NI) -> NI { + self.csr_out.degree(node) + } + + fn in_degree(&self, node: NI) -> NI { + self.csr_inc.degree(node) + } +} + +impl DirectedNeighbors for DirectedCsrGraph { + type NeighborsIterator<'a> = std::slice::Iter<'a, NI> where NV: 'a; + + fn out_neighbors(&self, node: NI) -> Self::NeighborsIterator<'_> { + self.csr_out.targets(node).iter() + } + + fn in_neighbors(&self, node: NI) -> Self::NeighborsIterator<'_> { + self.csr_inc.targets(node).iter() + } +} + +impl DirectedNeighborsWithValues for DirectedCsrGraph { + type NeighborsIterator<'a> = std::slice::Iter<'a, Target> where NV:'a, EV: 'a; + + fn out_neighbors_with_values(&self, node: NI) -> Self::NeighborsIterator<'_> { + self.csr_out.targets_with_values(node).iter() + } + + fn in_neighbors_with_values(&self, node: NI) -> Self::NeighborsIterator<'_> { + self.csr_inc.targets_with_values(node).iter() + } +} + +impl From<(E, CsrLayout)> for DirectedCsrGraph +where + NI: Idx, + EV: Copy + Send + Sync, + E: Edges, +{ + fn from((edge_list, csr_option): (E, CsrLayout)) -> Self { + let node_count = edge_list.max_node_id() + NI::new(1); + + let node_values = NodeValues::new(vec![(); node_count.index()]); + + let csr_out = Csr::from((&edge_list, node_count, Direction::Outgoing, csr_option)); + let csr_inc = Csr::from((&edge_list, node_count, Direction::Incoming, csr_option)); + + DirectedCsrGraph::new(node_values, csr_out, csr_inc) + } +} + +impl From<(NodeValues, E, CsrLayout)> for DirectedCsrGraph +where + NI: Idx, + EV: Copy + Send + Sync, + E: Edges, +{ + fn from((node_values, edge_list, csr_option): (NodeValues, E, CsrLayout)) -> Self { + let node_count = edge_list.max_node_id() + NI::new(1); + + assert!( + node_values.0.len() >= node_count.index(), + "number of node values ({}) does not match node count of edge list ({})", + node_values.0.len(), + node_count.index() + ); + + let csr_out = Csr::from((&edge_list, node_count, Direction::Outgoing, csr_option)); + let csr_inc = Csr::from((&edge_list, node_count, Direction::Incoming, csr_option)); + + DirectedCsrGraph::new(node_values, csr_out, csr_inc) + } +} + + +impl SerializeGraphOp for DirectedCsrGraph +where + W: Write, + NI: Idx + ToByteSlice, + NV: ToByteSlice, + EV: ToByteSlice, +{ + fn serialize(&self, mut output: W) -> Result<(), Error> { + let DirectedCsrGraph { + node_values, + csr_out, + csr_inc, + } = self; + + node_values.serialize(&mut output)?; + csr_out.serialize(&mut output)?; + csr_inc.serialize(&mut output)?; + + Ok(()) + } +} + +impl DeserializeGraphOp for DirectedCsrGraph +where + R: Read, + NI: Idx + ToMutByteSlice, + NV: ToMutByteSlice, + EV: ToMutByteSlice, +{ + fn deserialize(mut read: R) -> Result { + let node_values: NodeValues = NodeValues::deserialize(&mut read)?; + let csr_out: Csr = Csr::deserialize(&mut read)?; + let csr_inc: Csr = Csr::deserialize(&mut read)?; + Ok(DirectedCsrGraph::new(node_values, csr_out, csr_inc)) + } +} + +impl TryFrom<(PathBuf, CsrLayout)> for DirectedCsrGraph +where + NI: Idx + ToMutByteSlice, + EV: ToMutByteSlice, +{ + type Error = Error; + + fn try_from((path, _): (PathBuf, CsrLayout)) -> Result { + let reader = BufReader::new(File::open(path)?); + let graph = DirectedCsrGraph::deserialize(reader)?; + + Ok(graph) + } +} + +pub struct UndirectedCsrGraph { + node_values: NodeValues, + csr: Csr, +} + +impl From> for UndirectedCsrGraph { + fn from(csr: Csr) -> Self { + UndirectedCsrGraph::new(NodeValues::new(vec![(); csr.node_count().index()]), csr) + } +} + +impl UndirectedCsrGraph { + pub fn new(node_values: NodeValues, csr: Csr) -> Self { + let g = Self { node_values, csr }; + + g + } +} + +impl Graph for UndirectedCsrGraph { + fn node_count(&self) -> NI { + self.csr.node_count() + } + + fn edge_count(&self) -> NI { + self.csr.edge_count() / NI::new(2) + } +} + +impl NodeValuesTrait for UndirectedCsrGraph { + fn node_value(&self, node: NI) -> &NV { + &self.node_values.0[node.index()] + } +} + +impl UndirectedDegrees for UndirectedCsrGraph { + fn degree(&self, node: NI) -> NI { + self.csr.degree(node) + } +} + +impl UndirectedNeighbors for UndirectedCsrGraph { + type NeighborsIterator<'a> = std::slice::Iter<'a, NI> where NV: 'a; + + fn neighbors(&self, node: NI) -> Self::NeighborsIterator<'_> { + self.csr.targets(node).iter() + } +} + +impl UndirectedNeighborsWithValues for UndirectedCsrGraph { + type NeighborsIterator<'a> = std::slice::Iter<'a, Target> where NV: 'a, EV: 'a; + + fn neighbors_with_values(&self, node: NI) -> Self::NeighborsIterator<'_> { + self.csr.targets_with_values(node).iter() + } +} + +impl SwapCsr for UndirectedCsrGraph { + fn swap_csr(&mut self, mut csr: Csr) -> &mut Self { + std::mem::swap(&mut self.csr, &mut csr); + self + } +} + +impl From<(E, CsrLayout)> for UndirectedCsrGraph +where + NI: Idx, + EV: Copy + Send + Sync, + E: Edges, +{ + fn from((edge_list, csr_option): (E, CsrLayout)) -> Self { + let node_count = edge_list.max_node_id() + NI::new(1); + + let node_values = NodeValues::new(vec![(); node_count.index()]); + + let csr = Csr::from((&edge_list, node_count, Direction::Undirected, csr_option)); + + UndirectedCsrGraph::new(node_values, csr) + } +} + +impl From<(NodeValues, E, CsrLayout)> for UndirectedCsrGraph +where + NI: Idx, + EV: Copy + Send + Sync, + E: Edges, +{ + fn from((node_values, edge_list, csr_option): (NodeValues, E, CsrLayout)) -> Self { + let node_count = edge_list.max_node_id() + NI::new(1); + + let csr = Csr::from((&edge_list, node_count, Direction::Undirected, csr_option)); + + UndirectedCsrGraph::new(node_values, csr) + } +} + +impl SerializeGraphOp for UndirectedCsrGraph +where + W: Write, + NI: Idx + ToByteSlice, + NV: ToByteSlice, + EV: ToByteSlice, +{ + fn serialize(&self, mut output: W) -> Result<(), Error> { + let UndirectedCsrGraph { node_values, csr } = self; + + node_values.serialize(&mut output)?; + csr.serialize(&mut output)?; + + Ok(()) + } +} + +impl DeserializeGraphOp for UndirectedCsrGraph +where + R: Read, + NI: Idx + ToMutByteSlice, + NV: ToMutByteSlice, + EV: ToMutByteSlice, +{ + fn deserialize(mut read: R) -> Result { + let node_values = NodeValues::deserialize(&mut read)?; + let csr: Csr = Csr::deserialize(&mut read)?; + Ok(UndirectedCsrGraph::new(node_values, csr)) + } +} + +impl TryFrom<(PathBuf, CsrLayout)> for UndirectedCsrGraph +where + NI: Idx + ToMutByteSlice, + EV: ToMutByteSlice, +{ + type Error = Error; + + fn try_from((path, _): (PathBuf, CsrLayout)) -> Result { + let reader = BufReader::new(File::open(path)?); + UndirectedCsrGraph::deserialize(reader) + } +} + +fn prefix_sum_atomic(degrees: Vec>) -> Vec> { + let mut last = degrees.last().unwrap().load(Acquire); + let mut sums = degrees + .into_iter() + .scan(NI::zero(), |total, degree| { + let value = *total; + *total += degree.into_inner(); + Some(Atomic::new(value)) + }) + .collect::>(); + + last += sums.last().unwrap().load(Acquire); + sums.push(Atomic::new(last)); + + sums +} + +pub(crate) fn prefix_sum(degrees: Vec) -> Vec { + let mut last = *degrees.last().unwrap(); + let mut sums = degrees + .into_iter() + .scan(NI::zero(), |total, degree| { + let value = *total; + *total += degree; + Some(value) + }) + .collect::>(); + last += *sums.last().unwrap(); + sums.push(last); + sums +} + +pub(crate) fn sort_targets(offsets: &[NI], targets: &mut [Target]) +where + NI: Idx, + T: Copy + Send + Ord, + EV: Send, +{ + to_mut_slices(offsets, targets) + .par_iter_mut() + .for_each(|list| list.sort_unstable()); +} + +fn sort_and_deduplicate_targets( + offsets: &[NI], + targets: &mut [Target], +) -> (Vec, Vec>) +where + NI: Idx, + EV: Copy + Send, +{ + let node_count = offsets.len() - 1; + + let mut new_degrees = Vec::with_capacity(node_count); + let mut target_slices = to_mut_slices(offsets, targets); + + target_slices + .par_iter_mut() + .enumerate() + .map(|(node, slice)| { + slice.sort_unstable(); + // deduplicate + let (dedup, _) = slice.partition_dedup_compat(); + let mut new_degree = dedup.len(); + // remove self loops .. there is at most once occurence of node inside dedup + if let Ok(idx) = dedup.binary_search_by_key(&NI::new(node), |t| t.target) { + dedup[idx..].rotate_left(1); + new_degree -= 1; + } + NI::new(new_degree) + }) + .collect_into_vec(&mut new_degrees); + + let new_offsets = prefix_sum(new_degrees); + debug_assert_eq!(new_offsets.len(), node_count + 1); + + let edge_count = new_offsets[node_count].index(); + let mut new_targets: Vec> = Vec::with_capacity(edge_count); + let new_target_slices = to_mut_slices(&new_offsets, new_targets.spare_capacity_mut()); + + target_slices + .into_par_iter() + .zip(new_target_slices.into_par_iter()) + .for_each(|(old_slice, new_slice)| { + MaybeUninit::write_slice_compat(new_slice, &old_slice[..new_slice.len()]); + }); + + // SAFETY: We copied all (potentially shortened) target ids from the old + // target list to the new one. + unsafe { + new_targets.set_len(edge_count); + } + + (new_offsets, new_targets) +} + +fn to_mut_slices<'targets, NI: Idx, T>( + offsets: &[NI], + targets: &'targets mut [T], +) -> Vec<&'targets mut [T]> { + let node_count = offsets.len() - 1; + let mut target_slices = Vec::with_capacity(node_count); + let mut tail = targets; + let mut prev_offset = offsets[0]; + + for &offset in &offsets[1..] { + let (list, remainder) = tail.split_at_mut((offset - prev_offset).index()); + target_slices.push(list); + tail = remainder; + prev_offset = offset; + } + + target_slices +} diff --git a/packages/graph-rust/src/graph_builder/graph/mod.rs b/packages/graph-rust/src/graph_builder/graph/mod.rs new file mode 100644 index 0000000..571c09b --- /dev/null +++ b/packages/graph-rust/src/graph_builder/graph/mod.rs @@ -0,0 +1 @@ +pub mod csr; \ No newline at end of file diff --git a/packages/graph-rust/src/graph_builder/graph_ops.rs b/packages/graph-rust/src/graph_builder/graph_ops.rs new file mode 100644 index 0000000..06b2d9c --- /dev/null +++ b/packages/graph-rust/src/graph_builder/graph_ops.rs @@ -0,0 +1,627 @@ +use rayon::prelude::*; + +use super::graph::csr::{prefix_sum, Csr, SwapCsr, Target}; +use super::index::Idx; +use super::{ + CsrLayout, DirectedDegrees, DirectedNeighborsWithValues, Error, Graph, SharedMut, + UndirectedDegrees, UndirectedNeighborsWithValues, +}; + +use std::ops::Range; +use std::sync::Arc; + +/// Partition the node set based on the degrees of the nodes. +pub trait DegreePartitionOp { + /// Creates a range-based degree partition of the nodes. + /// + /// Divide the nodes into `concurrency` number of ranges such that these + /// ranges have roughly equal total degree. That is, the sum of the degrees + /// of the nodes of each range should be roughly equal to the extent that + /// that's actually possible. + /// The length of the returned vector will never exceed `concurrency`. + fn degree_partition(&self, concurrency: usize) -> Vec>; +} + +/// Partition the node set based on the out degrees of the nodes. +pub trait OutDegreePartitionOp { + /// Creates a range-based out degree partition of the nodes. + /// + /// Divide the nodes into `concurrency` number of ranges such that these + /// ranges have roughly equal total out degree. That is, the sum of the out + /// degrees of the nodes of each range should be roughly equal to the extent + /// that that's actually possible. + /// The length of the returned vector will never exceed `concurrency`. + fn out_degree_partition(&self, concurrency: usize) -> Vec>; +} + +/// Partition the node set based on the in degrees of the nodes. +pub trait InDegreePartitionOp { + /// Creates a range-based in degree partition of the nodes. + /// + /// Divide the nodes into `concurrency` number of ranges such that these + /// ranges have roughly equal total in degree. That is, the sum of the in + /// degrees of the nodes of each range should be roughly equal to the extent + /// that that's actually possible. + /// The length of the returned vector will never exceed `concurrency`. + fn in_degree_partition(&self, concurrency: usize) -> Vec>; +} + +/// Call a particular function for each node with its corresponding state in parallel. +pub trait ForEachNodeParallelOp { + /// For each node calls `node_fn` with the node and its corresponding mutable + /// state in parallel. + /// + /// For every node `n` in the graph `node_fn(&self, n, node_values[n.index()])` + /// will be called. + /// + /// `node_values` must have length exactly equal to the number of nodes in + /// the graph. + /// + /// # Example + /// + /// ``` + /// # use graph_builder::prelude::*; + /// # use std::ops::Range; + /// let graph: DirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(0, 1), (0, 2), (1, 2)]) + /// .build(); + /// let mut node_values = vec![0; 3]; + /// + /// graph. + /// for_each_node_par(&mut node_values, |g, node, node_state| { + /// *node_state = g.out_degree(node); + /// }); + /// + /// assert_eq!(node_values[0], 2); + /// assert_eq!(node_values[1], 1); + /// assert_eq!(node_values[2], 0); + /// ``` + fn for_each_node_par(&self, node_values: &mut [T], node_fn: F) -> Result<(), Error> + where + T: Send, + F: Fn(&Self, NI, &mut T) + Send + Sync; +} + +/// Call a particular function for each node with its corresponding state in parallel based on a +/// partition. +pub trait ForEachNodeParallelByPartitionOp { + /// For each node calls `node_fn` with the node and its corresponding + /// mutable state in parallel, using `partition` as a parallelization hint. + /// + /// For every node `n` in the graph `node_fn(&self, n, node_values[n.index()])` + /// will be called. + /// + /// `node_values` must have length exactly equal to the number of nodes in + /// the graph. + /// + /// The parallelization will be implemented such that the work for a set of + /// nodes represented by each range in `partition` will correspond to a task + /// that will run in a single thread. + /// + /// # Example + /// + /// ``` + /// # use graph_builder::prelude::*; + /// # use std::ops::Range; + /// let graph: DirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(0, 1), (0, 2), (1, 2)]) + /// .build(); + /// let mut node_values = vec![0; 3]; + /// let partition: Vec> = graph.out_degree_partition(num_cpus::get()); + /// + /// graph. + /// for_each_node_par_by_partition(&partition, &mut node_values, |g, node, node_state| { + /// *node_state = g.out_degree(node); + /// }); + /// + /// assert_eq!(node_values[0], 2); + /// assert_eq!(node_values[1], 1); + /// assert_eq!(node_values[2], 0); + /// ``` + fn for_each_node_par_by_partition( + &self, + partition: &[Range], + node_values: &mut [T], + node_fn: F, + ) -> Result<(), Error> + where + T: Send, + F: Fn(&Self, NI, &mut T) + Send + Sync; +} + +pub trait RelabelByDegreeOp { + /// Creates a new graph by relabeling the node ids of the given graph. + /// + /// Ids are relabaled using descending degree-order, i.e., given `n` nodes, + /// the node with the largest degree will become node id `0`, the node with + /// the smallest degree will become node id `n - 1`. + /// + /// Note, that this method creates a new graph with the same space + /// requirements as the input graph. + /// + /// # Example + /// + /// ``` + /// use graph_builder::prelude::*; + /// + /// let mut graph: UndirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(0, 1), (1, 2), (1, 3), (3, 0)]) + /// .build(); + /// + /// assert_eq!(graph.degree(0), 2); + /// assert_eq!(graph.degree(1), 3); + /// assert_eq!(graph.degree(2), 1); + /// assert_eq!(graph.degree(3), 2); + /// + /// let mut neighbors = graph.neighbors(0); + /// assert_eq!(neighbors.next(), Some(&1)); + /// assert_eq!(neighbors.next(), Some(&3)); + /// assert_eq!(neighbors.next(), None); + /// + /// graph.make_degree_ordered(); + /// + /// assert_eq!(graph.degree(0), 3); + /// assert_eq!(graph.degree(1), 2); + /// assert_eq!(graph.degree(2), 2); + /// assert_eq!(graph.degree(3), 1); + /// + /// assert_eq!(graph.neighbors(0).as_slice(), &[1, 2, 3]); + /// ``` + fn make_degree_ordered(&mut self); +} + +pub trait ToUndirectedOp { + type Undirected; + + /// Creates a new undirected graph from the edges of an existing graph. + /// + /// Note, that this method creates a new graph with the same space + /// requirements as the input graph. + /// + /// # Example + /// + /// ``` + /// use graph_builder::prelude::*; + /// + /// let graph: DirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(0, 1), (2, 0)]) + /// .build(); + /// + /// assert_eq!(graph.out_degree(0), 1); + /// assert_eq!(graph.out_neighbors(0).as_slice(), &[1]); + /// + /// assert_eq!(graph.in_degree(0), 1); + /// assert_eq!(graph.in_neighbors(0).as_slice(), &[2]); + /// + /// let graph = graph.to_undirected(None); + /// + /// assert_eq!(graph.degree(0), 2); + /// assert_eq!(graph.neighbors(0).as_slice(), &[1, 2]); + /// ``` + /// + /// This method accepts an optional [`CsrLayout`] as second parameter, + /// which has the same effect as described in [`GraphBuilder::csr_layout`] + + /// # Example + /// + /// ``` + /// use graph_builder::prelude::*; + /// + /// let graph: DirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(0, 2), (1, 0), (2, 0)]) + /// .build(); + /// + /// // No layout specified, a default layput is chosen + /// let un_graph = graph.to_undirected(None); + /// assert_eq!(un_graph.neighbors(0).as_slice(), &[2, 1, 2]); + /// + /// // The `Sorted` layout + /// let un_graph = graph.to_undirected(CsrLayout::Sorted); + /// assert_eq!(un_graph.neighbors(0).as_slice(), &[1, 2, 2]); + /// + /// // The `Deduplicated` layout + /// let un_graph = graph.to_undirected(CsrLayout::Deduplicated); + /// assert_eq!(un_graph.neighbors(0).as_slice(), &[1, 2]); + /// ``` + fn to_undirected(&self, layout: impl Into>) -> Self::Undirected; +} + +pub trait SerializeGraphOp { + fn serialize(&self, write: W) -> Result<(), Error>; +} + +pub trait DeserializeGraphOp { + fn deserialize(read: R) -> Result; +} + +impl RelabelByDegreeOp for G +where + NI: Idx, + EV: Copy + Ord + Sync, + G: Graph + + UndirectedDegrees + + UndirectedNeighborsWithValues + + SwapCsr + + Sync, +{ + fn make_degree_ordered(&mut self) { + relabel_by_degree(self) + } +} + +impl ForEachNodeParallelOp for G +where + NI: Idx, + G: Graph + Sync, +{ + /// For each node calls a given function with the node and its corresponding + /// mutable state in parallel. + /// + /// The parallelization is done by means of a [rayon](https://docs.rs/rayon/) + /// based fork join with a task for each node. + fn for_each_node_par(&self, node_values: &mut [T], node_fn: F) -> Result<(), Error> + where + T: Send, + F: Fn(&Self, NI, &mut T) + Send + Sync, + { + if node_values.len() != self.node_count().index() { + return Err(Error::InvalidNodeValues); + } + + let node_fn = Arc::new(node_fn); + + node_values + .into_par_iter() + .enumerate() + .for_each(|(i, node_state)| node_fn(self, NI::new(i), node_state)); + + Ok(()) + } +} + +impl ForEachNodeParallelByPartitionOp for G +where + NI: Idx, + G: Graph + Sync, +{ + /// For each node calls a given function with the node and its corresponding + /// mutable state in parallel based on the provided node partition. + /// + /// The parallelization is done by means of a [rayon](https://docs.rs/rayon/) + /// based fork join with a task for each range in the provided node partition. + fn for_each_node_par_by_partition( + &self, + partition: &[Range], + node_values: &mut [T], + node_fn: F, + ) -> Result<(), Error> + where + T: Send, + F: Fn(&Self, NI, &mut T) + Send + Sync, + { + if node_values.len() != self.node_count().index() { + return Err(Error::InvalidNodeValues); + } + + if partition.iter().map(|r| r.end - r.start).sum::() != self.node_count() { + return Err(Error::InvalidPartitioning); + } + + let node_fn = Arc::new(node_fn); + + let node_value_splits = split_by_partition(partition, node_values); + + node_value_splits + .into_par_iter() + .zip(partition.into_par_iter()) + .for_each_with(node_fn, |node_fn, (mutable_chunk, range)| { + for (node_state, node) in mutable_chunk.iter_mut().zip(range.start.range(range.end)) + { + node_fn(self, node, node_state); + } + }); + + Ok(()) + } +} + +impl DegreePartitionOp for U +where + NI: Idx, + U: Graph + UndirectedDegrees + UndirectedNeighborsWithValues, +{ + /// Creates a greedy range-based degree partition of the nodes. + /// + /// It is greedy in the sense that it goes through the node set only once + /// and simply adds a new range to the result whenever the current range's + /// nodes' degrees sum up to at least the average node degree. + /// + /// # Example + /// + /// ``` + /// # use graph_builder::prelude::*; + /// # use std::ops::Range; + /// let graph: UndirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(0, 1), (0, 2), (0, 3), (0, 3)]) + /// .build(); + /// + /// let partition: Vec> = graph.degree_partition(2); + /// + /// assert_eq!(partition.len(), 2); + /// assert_eq!(partition[0], 0..1); + /// assert_eq!(partition[1], 1..4); + /// ``` + fn degree_partition(&self, concurrency: usize) -> Vec> { + let batch_size = ((self.edge_count().index() * 2) as f64 / concurrency as f64).ceil(); + greedy_node_map_partition( + |node| self.degree(node).index(), + self.node_count(), + batch_size as usize, + concurrency, + ) + } +} + +impl OutDegreePartitionOp for D +where + NI: Idx, + D: Graph + DirectedDegrees + DirectedNeighborsWithValues, +{ + /// Creates a greedy range-based out degree partition of the nodes. + /// + /// It is greedy in the sense that it goes through the node set only once + /// and simply adds a new range to the result whenever the current range's + /// nodes' out degrees sum up to at least the average node out degree. + /// + /// # Example + /// + /// ``` + /// # use graph_builder::prelude::*; + /// # use std::ops::Range; + /// let graph: DirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(0, 1), (0, 2), (2, 1), (2, 3)]) + /// .build(); + /// + /// let partition: Vec> = graph.out_degree_partition(2); + /// + /// assert_eq!(partition.len(), 2); + /// assert_eq!(partition[0], 0..1); + /// assert_eq!(partition[1], 1..4); + /// ``` + fn out_degree_partition(&self, concurrency: usize) -> Vec> { + let batch_size = (self.edge_count().index() as f64 / concurrency as f64).ceil(); + greedy_node_map_partition( + |node| self.out_degree(node).index(), + self.node_count(), + batch_size as usize, + concurrency, + ) + } +} + +impl InDegreePartitionOp for D +where + NI: Idx, + D: Graph + DirectedDegrees + DirectedNeighborsWithValues, +{ + /// Creates a greedy range-based in degree partition of the nodes. + /// + /// It is greedy in the sense that it goes through the node set only once + /// and simply adds a new range to the result whenever the current range's + /// nodes' in degrees sum up to at least the average node in degree. + /// + /// # Example + /// + /// ``` + /// # use graph_builder::prelude::*; + /// # use std::ops::Range; + /// let graph: DirectedCsrGraph = GraphBuilder::new() + /// .edges(vec![(1, 0), (1, 2), (2, 0), (3, 2)]) + /// .build(); + /// + /// let partition: Vec> = graph.in_degree_partition(2); + /// + /// assert_eq!(partition.len(), 2); + /// assert_eq!(partition[0], 0..1); + /// assert_eq!(partition[1], 1..4); + /// ``` + fn in_degree_partition(&self, concurrency: usize) -> Vec> { + let batch_size = (self.edge_count().index() as f64 / concurrency as f64).ceil(); + greedy_node_map_partition( + |node| self.in_degree(node).index(), + self.node_count(), + batch_size as usize, + concurrency, + ) + } +} + +// Split input slice into a vector of partition.len() disjoint slices such that +// the slice at index i in the output vector has the same length as the range at +// index i in the input partition. +fn split_by_partition<'a, NI: Idx, T>( + partition: &[Range], + slice: &'a mut [T], +) -> Vec<&'a mut [T]> { + debug_assert_eq!( + partition + .iter() + .map(|r| r.end - r.start) + .sum::() + .index(), + slice.len() + ); + + let mut splits = Vec::with_capacity(partition.len()); + + let mut remainder = slice; + let mut current_start = NI::zero(); + for range in partition.iter() { + let next_end = range.end - current_start; + current_start += next_end; + + let (left, right) = remainder.split_at_mut(next_end.index()); + + splits.push(left); + remainder = right; + } + + splits +} + +// Partition nodes 0..node_count().index() into at most max_batches ranges such +// that the sums of node_map(node) for each range are roughly equal. It does so +// greedily and therefore does not guarantee an optimally balanced range-based +// partition. +fn greedy_node_map_partition( + node_map: F, + node_count: NI, + batch_size: usize, + max_batches: usize, +) -> Vec> +where + F: Fn(NI) -> usize, + NI: Idx, +{ + let mut partitions = Vec::with_capacity(max_batches); + + let mut partition_size = 0; + let mut partition_start = NI::zero(); + let upper_bound = node_count - NI::new(1); + + for node in NI::zero().range(node_count) { + partition_size += node_map(node); + + if (partitions.len() < max_batches - 1 && partition_size >= batch_size) + || node == upper_bound + { + let partition_end = node + NI::new(1); + partitions.push(partition_start..partition_end); + partition_size = 0; + partition_start = partition_end; + } + } + + partitions +} + +fn relabel_by_degree(graph: &mut G) +where + NI: Idx, + G: Graph + + UndirectedDegrees + + UndirectedNeighborsWithValues + + SwapCsr + + Sync, + EV: Copy + Ord + Sync, +{ + let degree_node_pairs = sort_by_degree_desc(graph); + let (degrees, nodes) = unzip_degrees_and_nodes(degree_node_pairs); + let offsets = prefix_sum(degrees); + let targets = relabel_targets(graph, nodes, &offsets); + + graph.swap_csr(Csr::new( + offsets.into_boxed_slice(), + targets.into_boxed_slice(), + )); +} + +// Extracts (degree, node_id) pairs from the given graph and sorts them by +// degree descending. +fn sort_by_degree_desc(graph: &G) -> Vec<(NI, NI)> +where + NI: Idx, + G: Graph + UndirectedDegrees + UndirectedNeighborsWithValues + Sync, +{ + let node_count = graph.node_count().index(); + let mut degree_node_pairs = Vec::with_capacity(node_count); + + (0..node_count) + .into_par_iter() + .map(NI::new) + .map(|node_id| (graph.degree(node_id), node_id)) + .collect_into_vec(&mut degree_node_pairs); + degree_node_pairs.par_sort_unstable_by(|left, right| left.cmp(right).reverse()); + + degree_node_pairs +} + +// Unzips (degree, node-id) pairs into `degrees` and `nodes` +// +// `degrees` maps a new node id to its degree. +// `nodes` maps the previous node id to the new node id. +fn unzip_degrees_and_nodes(degree_node_pairs: Vec<(NI, NI)>) -> (Vec, Vec) { + let node_count = degree_node_pairs.len(); + let mut degrees = Vec::::with_capacity(node_count); + let mut nodes = Vec::::with_capacity(node_count); + let nodes_ptr = SharedMut::new(nodes.as_mut_ptr()); + + (0..node_count) + .into_par_iter() + .map(|n| { + let (degree, node) = degree_node_pairs[n]; + + // SAFETY: node is the node_id from degree_node_pairs which is + // created from 0..node_count -- the values are all distinct and we + // will not write into the same location in parallel + unsafe { + nodes_ptr.add(node.index()).write(NI::new(n)); + } + + degree + }) + .collect_into_vec(&mut degrees); + + // SAFETY: degree_node_pairs contains each value in 0..node_count once + unsafe { + nodes.set_len(node_count); + } + + (degrees, nodes) +} + +// Relabel target ids according to the given node mapping and offsets. +fn relabel_targets(graph: &G, nodes: Vec, offsets: &[NI]) -> Vec> +where + NI: Idx, + G: Graph + UndirectedNeighborsWithValues + Sync, + EV: Copy + Ord + Sync, +{ + let node_count = graph.node_count().index(); + let edge_count = offsets[node_count].index(); + let mut targets = Vec::>::with_capacity(edge_count); + let targets_ptr = SharedMut::new(targets.as_mut_ptr()); + + (0..node_count).into_par_iter().map(NI::new).for_each(|u| { + let new_u = nodes[u.index()]; + let start_offset = offsets[new_u.index()].index(); + let mut end_offset = start_offset; + + for &v in graph.neighbors_with_values(u) { + let new_v = nodes[v.target.index()]; + // SAFETY: a node u is processed by at most one thread. We write + // into a non-overlapping range defined by the offsets for that + // node. No two threads will write into the same range. + unsafe { + targets_ptr + .add(end_offset) + .write(Target::new(new_v, v.value)); + } + end_offset += 1; + } + + // SAFETY: start_offset..end_offset is a non-overlapping range for + // a node u which is processed by exactly one thread. + unsafe { + std::slice::from_raw_parts_mut(targets_ptr.add(start_offset), end_offset - start_offset) + } + .sort_unstable(); + }); + + // SAFETY: we inserted every relabeled target id of which there are edge_count many. + unsafe { + targets.set_len(edge_count); + } + + targets +} diff --git a/packages/graph-rust/src/graph_builder/index.rs b/packages/graph-rust/src/graph_builder/index.rs new file mode 100644 index 0000000..8f7c7c0 --- /dev/null +++ b/packages/graph-rust/src/graph_builder/index.rs @@ -0,0 +1,103 @@ +use std::fmt::Debug; +use std::iter::Sum; +use std::ops::{Range, RangeInclusive}; +use std::sync::atomic::Ordering; + +use atoi::FromRadix10; +use atomic::Atomic; + +pub trait Idx: + Copy + + std::ops::Add + + std::ops::AddAssign + + std::ops::Sub + + std::ops::Div + + std::ops::Mul + + Ord + + Debug + + Send + + Sum + + Sync + + Sized + + 'static +{ + fn new(idx: usize) -> Self; + + fn zero() -> Self; + + fn index(self) -> usize; + + type RangeIter: Iterator; + + fn range(self, end: Self) -> Self::RangeIter; + + type RangeInclusiveIter: Iterator; + + fn range_inclusive(self, end: Self) -> Self::RangeInclusiveIter; + + fn parse(bytes: &[u8]) -> (Self, usize); + + fn get_and_increment(this: &Atomic, order: Ordering) -> Self { + Self::fetch_add(this, Self::new(1), order) + } + + fn fetch_add(this: &Atomic, val: Self, order: Ordering) -> Self; +} + +macro_rules! impl_idx { + ($TYPE:ty) => { + impl Idx for $TYPE { + #[inline] + fn new(idx: usize) -> Self { + assert!(idx <= <$TYPE>::MAX as usize); + idx as $TYPE + } + + #[inline] + fn zero() -> Self { + 0 + } + + #[inline] + fn index(self) -> usize { + self as usize + } + + type RangeIter = Range; + + #[inline] + fn range(self, end: Self) -> Self::RangeIter { + self..end + } + + type RangeInclusiveIter = RangeInclusive; + + #[inline] + fn range_inclusive(self, end: Self) -> Self::RangeInclusiveIter { + self..=end + } + + #[inline] + fn parse(bytes: &[u8]) -> (Self, usize) { + FromRadix10::from_radix_10(bytes) + } + + #[inline] + fn fetch_add(this: &Atomic<$TYPE>, val: $TYPE, order: Ordering) -> $TYPE { + this.fetch_add(val, order) + } + } + }; +} + +impl_idx!(u8); +impl_idx!(u16); +impl_idx!(u32); +impl_idx!(u64); +impl_idx!(usize); + +impl_idx!(i8); +impl_idx!(i16); +impl_idx!(i32); +impl_idx!(i64); +impl_idx!(isize); \ No newline at end of file diff --git a/packages/graph-rust/src/graph_builder/input/edgelist.rs b/packages/graph-rust/src/graph_builder/input/edgelist.rs new file mode 100644 index 0000000..552d4a7 --- /dev/null +++ b/packages/graph-rust/src/graph_builder/input/edgelist.rs @@ -0,0 +1,258 @@ +use atomic::Atomic; +use std::{convert::TryFrom, fs::File, marker::PhantomData, path::Path, sync::Arc}; + +use parking_lot::Mutex; +use rayon::prelude::*; +use std::sync::atomic::Ordering::AcqRel; + +use crate::prelude::{Idx, Error}; + +use super::{InputCapabilities, InputPath, ParseValue, Direction}; + +/// Reads a graph from a file that contains an edge per line. +/// +/// An edge is represented by a source node id and a target node id. The two +/// node ids must be separated by a 1-byte character (e.g. whitespace or tab). +/// +/// The node count of the resulting graph is the highest node id within the file +/// plus one. The edge count will be twice the number of lines in the file. +/// +/// # Example +/// +/// ```ignore +/// > cat my_graph.edgelist +/// 0 1 +/// 0 2 +/// 1 3 +/// 2 0 +/// ``` +pub struct EdgeListInput { + _idx: PhantomData<(NI, EV)>, +} + +impl Default for EdgeListInput { + fn default() -> Self { + Self { _idx: PhantomData } + } +} + +impl InputCapabilities for EdgeListInput { + type GraphInput = EdgeList; +} + +#[allow(clippy::len_without_is_empty)] +pub trait Edges { + type NI: Idx; + type EV; + + type EdgeIter<'a>: ParallelIterator + where + Self: 'a; + + fn edges(&self) -> Self::EdgeIter<'_>; + + fn max_node_id(&self) -> Self::NI { + default_max_node_id(self) + } + + fn degrees(&self, node_count: Self::NI, direction: Direction) -> Vec> { + let mut degrees = Vec::with_capacity(node_count.index()); + degrees.resize_with(node_count.index(), || Atomic::new(Self::NI::zero())); + + if matches!(direction, Direction::Outgoing | Direction::Undirected) { + self.edges().for_each(|(s, _, _)| { + Self::NI::get_and_increment(°rees[s.index()], AcqRel); + }); + } + + if matches!(direction, Direction::Incoming | Direction::Undirected) { + self.edges().for_each(|(_, t, _)| { + Self::NI::get_and_increment(°rees[t.index()], AcqRel); + }); + } + + degrees + } + + #[cfg(test)] + fn len(&self) -> usize; +} + +fn default_max_node_id(edges: &E) -> E::NI { + edges + .edges() + .into_par_iter() + .map(|(s, t, _)| E::NI::max(s, t)) + .reduce(E::NI::zero, E::NI::max) +} + +#[derive(Debug)] +pub struct EdgeList { + list: Box<[(NI, NI, EV)]>, + max_node_id: Option, +} + +impl EdgeList { + pub fn new(edges: Vec<(NI, NI, EV)>) -> Self { + Self { + list: edges.into_boxed_slice(), + max_node_id: None, + } + } + + pub fn with_max_node_id(edges: Vec<(NI, NI, EV)>, max_node_id: NI) -> Self { + Self { + list: edges.into_boxed_slice(), + max_node_id: Some(max_node_id), + } + } +} + +impl Edges for EdgeList { + type NI = NI; + + type EV = EV; + + type EdgeIter<'a> = rayon::iter::Copied> + where + Self: 'a; + + fn edges(&self) -> Self::EdgeIter<'_> { + self.list.into_par_iter().copied() + } + + #[cfg(test)] + fn len(&self) -> usize { + self.list.len() + } + + fn max_node_id(&self) -> Self::NI { + match self.max_node_id { + Some(id) => id, + None => default_max_node_id(self), + } + } +} + +pub(crate) struct EdgeIterator>(pub I); + +impl From> for EdgeList +where + NI: Idx, + I: IntoIterator, +{ + fn from(iter: EdgeIterator) -> Self { + EdgeList::new(iter.0.into_iter().map(|(s, t)| (s, t, ())).collect()) + } +} + +pub(crate) struct EdgeWithValueIterator>(pub I); + +impl From> for EdgeList +where + NI: Idx, + EV: Sync, + I: IntoIterator, +{ + fn from(iter: EdgeWithValueIterator) -> Self { + EdgeList::new(iter.0.into_iter().map(|(s, t, v)| (s, t, v)).collect()) + } +} + +impl TryFrom> for EdgeList +where + P: AsRef, + NI: Idx, + EV: ParseValue + std::fmt::Debug + Send + Sync, +{ + type Error = Error; + + fn try_from(path: InputPath

) -> Result { + let file = File::open(path.0.as_ref())?; + let mmap = unsafe { memmap2::MmapOptions::new().populate().map(&file)? }; + EdgeList::try_from(mmap.as_ref()) + } +} + +impl TryFrom<&[u8]> for EdgeList +where + NI: Idx, + EV: ParseValue + std::fmt::Debug + Send + Sync, +{ + type Error = Error; + + fn try_from(bytes: &[u8]) -> Result { + let page_size = page_size::get(); + let cpu_count = num_cpus::get_physical(); + let chunk_size = + (usize::max(1, bytes.len() / cpu_count) + (page_size - 1)) & !(page_size - 1); + + let all_edges = Arc::new(Mutex::new(Vec::new())); + + let new_line_bytes = new_line_bytes(bytes); + + rayon::scope(|s| { + for start in (0..bytes.len()).step_by(chunk_size) { + let all_edges = Arc::clone(&all_edges); + s.spawn(move |_| { + let mut end = usize::min(start + chunk_size, bytes.len()); + while end <= bytes.len() && bytes[end - 1] != b'\n' { + end += 1; + } + + let mut start = start; + if start != 0 { + while bytes[start - 1] != b'\n' { + start += 1; + } + } + + let mut edges = Vec::new(); + let mut chunk = &bytes[start..end]; + while !chunk.is_empty() { + let (source, source_bytes) = NI::parse(chunk); + chunk = &chunk[source_bytes + 1..]; + + let (target, target_bytes) = NI::parse(chunk); + chunk = &chunk[target_bytes..]; + + let value = match chunk.strip_prefix(b" ") { + Some(value_chunk) => { + let (value, value_bytes) = EV::parse(value_chunk); + chunk = &value_chunk[value_bytes + new_line_bytes..]; + value + } + None => { + chunk = &chunk[new_line_bytes..]; + // if the input does not have a value, the default for EV is used + EV::parse(&[]).0 + } + }; + + edges.push((source, target, value)); + } + + let mut all_edges = all_edges.lock(); + all_edges.append(&mut edges); + }); + } + }); + + let edges = Arc::try_unwrap(all_edges).unwrap().into_inner(); + + Ok(EdgeList::new(edges)) + } +} + +// Returns the OS-dependent number of bytes for newline: +// +// `1` for Linux/macOS style (b'\n') +// '2' for Windows style (b'\r\n') +fn new_line_bytes(bytes: &[u8]) -> usize { + 1 + bytes + .iter() + .position(|b| *b == b'\n') + .and_then(|idx| idx.checked_sub(1)) + .and_then(|idx| bytes.get(idx).copied()) + .map_or(0, |b| (b == b'\r') as usize) +} diff --git a/packages/graph-rust/src/graph_builder/input/mod.rs b/packages/graph-rust/src/graph_builder/input/mod.rs new file mode 100644 index 0000000..77ea902 --- /dev/null +++ b/packages/graph-rust/src/graph_builder/input/mod.rs @@ -0,0 +1,93 @@ +pub mod edgelist; + + +pub use edgelist::EdgeList; +pub use edgelist::EdgeListInput; +pub use edgelist::Edges; + +use super::index::Idx; + +pub struct InputPath

(pub(crate) P); + +pub trait InputCapabilities { + type GraphInput; +} + +#[derive(Debug, Clone, Copy)] +pub enum Direction { + Outgoing, + Incoming, + Undirected, +} + +/// Used by input formats to read node or edge values from bytes. +pub trait ParseValue: Default + Sized { + /// Parses a value from a slice. + /// + /// # Example + /// + /// ``` + /// use graph_builder::input::ParseValue; + /// + /// let bytes = "13.37".as_bytes(); + /// + /// let (number, len) = f32::parse(bytes); + /// + /// assert_eq!(number, 13.37); + /// assert_eq!(len, 5); + /// ``` + /// + /// # Return + /// + /// Returns a tuple containing two entries. The first is the parsed value, + /// the second is the index of the byte right after the parsed value. + fn parse(bytes: &[u8]) -> (Self, usize); +} + +impl ParseValue for () { + fn parse(_bytes: &[u8]) -> (Self, usize) { + ((), 0) + } +} + +macro_rules! impl_parse_value { + ($atoi:path, $($ty:ty),+ $(,)?) => { + $( + impl $crate::graph_builder::input::ParseValue for $ty { + fn parse(bytes: &[u8]) -> (Self, usize) { + if bytes.len() == 0 { + (<$ty as ::std::default::Default>::default(), 0) + } else { + $atoi(bytes) + } + } + } + )+ + }; +} + +impl_parse_value!( + ::atoi::FromRadix10::from_radix_10, + u8, + u16, + u32, + u64, + u128, + usize, +); + +impl_parse_value!( + ::atoi::FromRadix10Signed::from_radix_10_signed, + i8, + i16, + i32, + i64, + i128, + isize, +); + +impl_parse_value!(parse_float, f32, f64); + +fn parse_float(bytes: &[u8]) -> (T, usize) { + fast_float::parse_partial(bytes).unwrap() +} \ No newline at end of file diff --git a/packages/graph-rust/src/graph_builder/mod.rs b/packages/graph-rust/src/graph_builder/mod.rs new file mode 100644 index 0000000..62aeab7 --- /dev/null +++ b/packages/graph-rust/src/graph_builder/mod.rs @@ -0,0 +1,171 @@ + + +pub mod builder; +mod compat; +pub mod graph; +pub mod graph_ops; +pub mod index; +pub mod input; +pub mod prelude; + +pub use self::builder::GraphBuilder; +pub use self::graph::csr::CsrLayout; +pub use self::graph::csr::DirectedCsrGraph; +pub use self::graph::csr::UndirectedCsrGraph; + +use std::convert::Infallible; + +use self::graph::csr::Target; +use thiserror::Error; + +use self::index::Idx; + +#[derive(Error, Debug)] +pub enum Error { + #[error("error while loading graph")] + IoError { + #[from] + source: std::io::Error, + }, + #[error("incompatible index type")] + IdxError { + #[from] + source: std::num::TryFromIntError, + }, + #[error("invalid partitioning")] + InvalidPartitioning, + #[error("number of node values must be the same as node count")] + InvalidNodeValues, + #[error("invalid id size, expected {expected:?} bytes, got {actual:?} bytes")] + InvalidIdType { expected: String, actual: String }, +} + +impl From for Error { + fn from(_: Infallible) -> Self { + unreachable!() + } +} + +/// A graph is a tuple `(N, E)`, where `N` is a set of nodes and `E` a set of +/// edges. Each edge connects exactly two nodes. +/// +/// `Graph` is parameterized over the node index type `Node` which is used to +/// uniquely identify a node. An edge is a tuple of node identifiers. +pub trait Graph { + /// Returns the number of nodes in the graph. + fn node_count(&self) -> NI; + + /// Returns the number of edges in the graph. + fn edge_count(&self) -> NI; +} + +/// A graph that allows storing a value per node. +pub trait NodeValues { + fn node_value(&self, node: NI) -> &NV; +} + +pub trait UndirectedDegrees { + /// Returns the number of edges connected to the given node. + fn degree(&self, node: NI) -> NI; +} + +/// Returns the neighbors of a given node. +/// +/// The edge `(42, 1337)` is equivalent to the edge `(1337, 42)`. +pub trait UndirectedNeighbors { + type NeighborsIterator<'a>: Iterator + where + Self: 'a; + + /// Returns an iterator of all nodes connected to the given node. + fn neighbors(&self, node: NI) -> Self::NeighborsIterator<'_>; +} + +/// Returns the neighbors of a given node. +/// +/// The edge `(42, 1337)` is equivalent to the edge `(1337, 42)`. +pub trait UndirectedNeighborsWithValues { + type NeighborsIterator<'a>: Iterator> + where + Self: 'a, + EV: 'a; + + /// Returns an iterator of all nodes connected to the given node + /// including the value of the connecting edge. + fn neighbors_with_values(&self, node: NI) -> Self::NeighborsIterator<'_>; +} + +pub trait DirectedDegrees { + /// Returns the number of edges where the given node is a source node. + fn out_degree(&self, node: NI) -> NI; + + /// Returns the number of edges where the given node is a target node. + fn in_degree(&self, node: NI) -> NI; +} + +/// Returns the neighbors of a given node either in outgoing or incoming direction. +/// +/// An edge tuple `e = (u, v)` has a source node `u` and a target node `v`. From +/// the perspective of `u`, the edge `e` is an **outgoing** edge. From the +/// perspective of node `v`, the edge `e` is an **incoming** edge. The edges +/// `(u, v)` and `(v, u)` are not considered equivalent. +pub trait DirectedNeighbors { + type NeighborsIterator<'a>: Iterator + where + Self: 'a; + + /// Returns an iterator of all nodes which are connected in outgoing direction + /// to the given node, i.e., the given node is the source node of the + /// connecting edge. + fn out_neighbors(&self, node: NI) -> Self::NeighborsIterator<'_>; + + /// Returns an iterator of all nodes which are connected in incoming direction + /// to the given node, i.e., the given node is the target node of the + /// connecting edge. + fn in_neighbors(&self, node: NI) -> Self::NeighborsIterator<'_>; +} + +/// Returns the neighbors of a given node either in outgoing or incoming direction. +/// +/// An edge tuple `e = (u, v)` has a source node `u` and a target node `v`. From +/// the perspective of `u`, the edge `e` is an **outgoing** edge. From the +/// perspective of node `v`, the edge `e` is an **incoming** edge. The edges +/// `(u, v)` and `(v, u)` are not considered equivale +pub trait DirectedNeighborsWithValues { + type NeighborsIterator<'a>: Iterator> + where + Self: 'a, + EV: 'a; + + /// Returns an iterator of all nodes which are connected in outgoing direction + /// to the given node, i.e., the given node is the source node of the + /// connecting edge. For each connected node, the value of the connecting + /// edge is also returned. + fn out_neighbors_with_values(&self, node: NI) -> Self::NeighborsIterator<'_>; + + /// Returns an iterator of all nodes which are connected in incoming direction + /// to the given node, i.e., the given node is the target node of the + /// connecting edge. For each connected node, the value of the connecting + /// edge is also returned. + fn in_neighbors_with_values(&self, node: NI) -> Self::NeighborsIterator<'_>; +} + +#[repr(transparent)] +pub struct SharedMut(*mut T); +unsafe impl Send for SharedMut {} +unsafe impl Sync for SharedMut {} + +impl SharedMut { + pub fn new(ptr: *mut T) -> Self { + SharedMut(ptr) + } + + delegate::delegate! { + to self.0 { + /// # Safety + /// + /// Ensure that `count` does not exceed the capacity of the Vec. + pub unsafe fn add(&self, count: usize) -> *mut T; + } + } +} \ No newline at end of file diff --git a/packages/graph-rust/src/graph_builder/prelude.rs b/packages/graph-rust/src/graph_builder/prelude.rs new file mode 100644 index 0000000..a6a225c --- /dev/null +++ b/packages/graph-rust/src/graph_builder/prelude.rs @@ -0,0 +1,32 @@ +pub use super::builder::GraphBuilder; + +pub use super::graph::csr::CsrLayout; +pub use super::graph::csr::DirectedCsrGraph; +pub use super::graph::csr::Target; +pub use super::graph::csr::UndirectedCsrGraph; + +pub use super::graph_ops::DegreePartitionOp; +pub use super::graph_ops::DeserializeGraphOp; +pub use super::graph_ops::ForEachNodeParallelByPartitionOp; +pub use super::graph_ops::ForEachNodeParallelOp; +pub use super::graph_ops::InDegreePartitionOp; +pub use super::graph_ops::OutDegreePartitionOp; +pub use super::graph_ops::RelabelByDegreeOp; +pub use super::graph_ops::SerializeGraphOp; +pub use super::graph_ops::ToUndirectedOp; + +pub use super::index::Idx; +pub use atomic::Atomic; + +pub use super::input::*; + +pub use super::DirectedDegrees; +pub use super::DirectedNeighbors; +pub use super::DirectedNeighborsWithValues; +pub use super::Graph; +pub use super::NodeValues; +pub use super::UndirectedDegrees; +pub use super::UndirectedNeighbors; +pub use super::UndirectedNeighborsWithValues; + +pub use super::Error; \ No newline at end of file diff --git a/packages/graph-rust/src/lib.rs b/packages/graph-rust/src/lib.rs new file mode 100644 index 0000000..e8b9db8 --- /dev/null +++ b/packages/graph-rust/src/lib.rs @@ -0,0 +1,8 @@ +pub mod page_rank; +pub mod prelude; +pub mod sssp; +pub mod louvain; +pub mod utils; +pub mod graph_builder; + +const DEFAULT_PARALLELISM: usize = 4; diff --git a/packages/graph-rust/src/louvain.rs b/packages/graph-rust/src/louvain.rs new file mode 100644 index 0000000..767f863 --- /dev/null +++ b/packages/graph-rust/src/louvain.rs @@ -0,0 +1,604 @@ +#![allow(non_snake_case)] // FIXME: Good while porting + +use super::graph_builder::{prelude::*}; +use std::collections::{BTreeMap}; +use std::cell::{RefCell}; +use std::ops::{AddAssign}; +use slotmap::DenseSlotMap; +use std::f32; + +#[derive(Debug, PartialEq)] +pub struct ModEdge { + source: usize, + target: usize, + weight: f32, +} + +type CommunityId = slotmap::DefaultKey; + +#[derive(Debug, Default, PartialEq)] +pub struct Community { + id: CommunityId, + weightSum: f64, + nodes: Vec, + connectionsWeight: RefCell>, + connectionsCount: RefCell>, +} + +impl Community { + + fn new(id: CommunityId) -> Community { + Community { id, ..Default::default() } + } + + fn size(&self) -> usize { + self.nodes.len() + } + + fn seed(&mut self, node: usize, cs: & CommunityStructure) { + self.nodes.push(node); + self.weightSum += cs.weights[node] as f64; + } + + fn add(&mut self, node: usize, cs: & CommunityStructure) -> bool { + self.nodes.push(node); + self.weightSum += cs.weights[node] as f64; + true + } + + fn remove(&mut self, node: usize, cs: &mut CommunityStructure) -> bool { + self.nodes.retain(|&n| n != node); + self.weightSum -= cs.weights[node] as f64; + if self.nodes.is_empty() { + cs.communities.retain(|&c| c != self.id); // FIXME: Maybe remove from cc too + } + true + } +} + + +#[derive(Default, Debug)] +pub struct CommunityCatalog { + map: DenseSlotMap +} + +impl CommunityCatalog { + pub fn createNew(&mut self) -> CommunityId { + self.map.insert_with_key(|id| Community::new(id)) + } + + #[inline] + pub fn get(&self, key: &CommunityId) -> Option<&Community> { + self.map.get(*key) + } + + #[inline] + pub fn get_mut(&mut self, key: &CommunityId) -> Option<&mut Community> { + self.map.get_mut(*key) + } + + #[inline] + pub fn remove(&mut self, key: &CommunityId) { + self.map.remove(*key); + } +} + + +#[derive(Debug, Default, PartialEq)] +pub struct CommunityStructure { + N: usize, // Number of nodes / communities + communities: Vec, // Direct communities + nodeConnectionsWeight: Vec>, + nodeConnectionsCount: Vec>, + nodeCommunities: Vec, + weights: Vec, // One per node + topology: Vec>, // One per node + invMap: BTreeMap, + graphWeightSum: f64, +} + +impl CommunityStructure { + + pub fn new(graph: &G, modularity: &mut Modularity) -> CommunityStructure where + NI: Idx, + G: Graph + DirectedDegrees + DirectedNeighborsWithValues + Sync { + let N = graph.node_count().index(); + let cc = &mut modularity.cc; + let mut cs = CommunityStructure { + N, + communities: Vec::new(), + nodeConnectionsWeight: Vec::with_capacity(N), + nodeConnectionsCount: Vec::with_capacity(N), + nodeCommunities: Vec::with_capacity(N), + weights: Vec::with_capacity(N), + topology: Vec::with_capacity(N), + //map: BTreeMap::default(), + invMap: BTreeMap::default(), + graphWeightSum: 0.0, + }; + + // Create one community and one inverse community per node + // All weights to 0.0 + (0..N).into_iter().enumerate().for_each(|(_, index)| { + //cs.map.insert(node.clone(), index); + cs.nodeCommunities.push( cc.createNew() ); + + cs.nodeConnectionsWeight.push(BTreeMap::default()); + cs.nodeConnectionsCount.push(BTreeMap::default()); + cs.weights.push(0.0); + cc.get_mut(& cs.nodeCommunities[index]).unwrap().seed(index, & cs); + + // New hidden community + let hidden = cc.createNew(); + cc.get_mut(& hidden).unwrap().nodes.push(index); + cs.invMap.insert(index, hidden); + + cs.communities.push(cs.nodeCommunities[index]); + }); + + (0..N) + .into_iter() + .map(NI::new) + .for_each(|node| { + cs.topology.push(Vec::new()); + + for Target { target, value } in graph.out_neighbors_with_values(node) { + let neighbor = target; + if node == *neighbor { continue } + + // Assume there is no parallel edges: + let weight: f32 = *value; + + //Finally add a single edge with the summed weight of all parallel edges: + cs.weights[node.index()] += weight as f64; + let modularity_edge = ModEdge { source: node.index(), target: neighbor.index(), weight }; + cs.topology[node.index()].push(modularity_edge); + let adjCom = cc.get(&cs.nodeCommunities[neighbor.index()]).unwrap(); + + cs.nodeConnectionsWeight[node.index()].insert(adjCom.id, weight); + cs.nodeConnectionsCount[node.index()].insert(adjCom.id, 1); + + let nodeCom = cc.get(&cs.nodeCommunities[node.index()]).unwrap(); + nodeCom.connectionsWeight.borrow_mut().insert(adjCom.id, weight); + nodeCom.connectionsCount.borrow_mut().insert(adjCom.id, 1); + + cs.nodeConnectionsWeight[neighbor.index()].insert(nodeCom.id, weight); + cs.nodeConnectionsCount[neighbor.index()].insert(nodeCom.id, 1); + + adjCom.connectionsWeight.borrow_mut().insert(nodeCom.id, weight); + adjCom.connectionsCount.borrow_mut().insert(nodeCom.id, 1); + + cs.graphWeightSum += weight as f64; + } + }); + + cs.graphWeightSum /= 2.0; + cs + } + + fn addNodeTo(&mut self, node : usize, com_id: CommunityId, cc: &mut CommunityCatalog) { + + #[inline] + fn add_node >(map : &mut BTreeMap, key: CommunityId, weight: V) { + let w = map.entry(key).or_insert(V::from(0)); + *w += weight; + } + + self.nodeCommunities[node] = com_id; + + { + let com = cc.get_mut(& com_id).unwrap(); + com.add(node, self); + } + + for e in &self.topology[node] { + let neighbor = &e.target; + + add_node(&mut self.nodeConnectionsWeight[*neighbor], com_id, e.weight); + add_node(&mut self.nodeConnectionsCount[*neighbor], com_id, 1); + + let adjCom = cc.get(& self.nodeCommunities[*neighbor]).unwrap(); + + add_node(&mut adjCom.connectionsWeight.borrow_mut(), com_id, e.weight); + add_node(&mut adjCom.connectionsCount.borrow_mut(), com_id, 1); + + add_node(&mut self.nodeConnectionsWeight[node], adjCom.id, e.weight); + add_node(&mut self.nodeConnectionsCount[node], adjCom.id, 1); + + if com_id != adjCom.id { + let com = cc.get(& com_id).unwrap(); + add_node(&mut com.connectionsWeight.borrow_mut(), adjCom.id, e.weight); + add_node(&mut com.connectionsCount.borrow_mut(), adjCom.id, 1); + } + + } + + + } + + + pub fn removeNodeFromItsCommunity(&mut self, node: usize, cc: &mut CommunityCatalog) { + + #[inline] + fn remove_node( weights_map : &mut BTreeMap, + count_map : &mut BTreeMap, + key: CommunityId, weight: f32 + ) + { + + // println!("*** remove community {} from {:?}", key, count_map); + let count = count_map.get(&key).unwrap().clone(); + + if count -1 == 0 { + weights_map.remove(&key); + count_map.remove(&key); + } else { + let count = count_map.get_mut(&key).unwrap(); + *count -= 1; + let w = weights_map.get_mut(&key).unwrap(); + *w -= weight; + } + } + + { + let community = cc.get(& self.nodeCommunities[node]).unwrap(); + + for e in &self.topology[node] { + let neighbor = &e.target; + + //////// + //Remove Node Connection to this community + remove_node( &mut self.nodeConnectionsWeight[*neighbor], + &mut self.nodeConnectionsCount[*neighbor], + community.id.clone(), e.weight ); + + /////////////////// + //Remove Adjacency Community's connection to this community + let adjCom = cc.get(& self.nodeCommunities[*neighbor]).unwrap(); + remove_node( &mut adjCom.connectionsWeight.borrow_mut(), + &mut adjCom.connectionsCount.borrow_mut(), + community.id.clone(), e.weight ); + + if node == *neighbor { + continue; + } + + if adjCom.id != community.id { + remove_node( &mut community.connectionsWeight.borrow_mut(), + &mut community.connectionsCount.borrow_mut(), + adjCom.id, e.weight); + } + + remove_node( &mut self.nodeConnectionsWeight[node], + &mut self.nodeConnectionsCount[node], + adjCom.id, e.weight ); + } + } + { + let community = cc.get_mut(& self.nodeCommunities[node]).unwrap(); + community.remove(node, self); + } + } + + + fn moveNodeTo(&mut self, node: usize, to: CommunityId, cc: &mut CommunityCatalog) { + self.removeNodeFromItsCommunity(node, cc); + self.addNodeTo(node, to, cc); + } + + fn zoomOut(&mut self, cc: &mut CommunityCatalog) { + let M = self.communities.len(); + self.communities.sort(); + let mut newTopology: Vec> = Vec::with_capacity(M); + let mut index : usize = 0; + let mut nodeCommunities: Vec = Vec::with_capacity(M); + let mut nodeConnectionsWeight: Vec> = Vec::with_capacity(M); + let mut nodeConnectionsCount: Vec> = Vec::with_capacity(M); + let mut newInvMap: BTreeMap = BTreeMap::default(); + + for com_id in & self.communities { + nodeConnectionsWeight.push(BTreeMap::default()); + nodeConnectionsCount.push(BTreeMap::default()); + + newTopology.push( Vec::new() ); + nodeCommunities.push(cc.createNew()); + + let mut weightSum : f32 = 0.0; + let hidden_id = cc.createNew(); + + /* + * FIXME: two unnecessary clones of node vectors to content the Borrow Checker + */ + let nodes = cc.get(com_id).unwrap().nodes.clone(); + for nodeInt in nodes { + let oldHiddenNodes = { + let oldHidden = cc.get(& self.invMap.get(&nodeInt).unwrap()).unwrap(); + oldHidden.nodes.clone() + }; + let hidden = cc.get_mut(& hidden_id).unwrap(); + hidden.nodes.extend(oldHiddenNodes); + } + + newInvMap.insert(index, hidden_id); + { + let com = cc.get(com_id).unwrap(); + for adjCom_id in com.connectionsWeight.borrow().keys() { + let target = self.communities.binary_search(adjCom_id).unwrap(); + let weight = com.connectionsWeight.borrow().get(adjCom_id).unwrap().clone(); + weightSum += if target == index { 2.0 * weight} else { weight }; + + let e = ModEdge {source: index, target: target, weight: weight}; + newTopology[index].push(e); + } + } + self.weights[index] = weightSum as f64; + let com = cc.get_mut(& nodeCommunities[index]).unwrap(); + com.seed(index, & self); + + index += 1; + } + + for com_id in & self.communities { cc.remove(com_id); } + self.communities.clear(); + + for i in 0..M { + let com = cc.get(& nodeCommunities[i]).unwrap(); + self.communities.push(com.id); + for e in & newTopology[i] { + nodeConnectionsWeight[i].insert(nodeCommunities[e.target], e.weight); + nodeConnectionsCount[i].insert(nodeCommunities[e.target], 1); + com.connectionsWeight.borrow_mut().insert(nodeCommunities[e.target], e.weight); + com.connectionsCount.borrow_mut().insert(nodeCommunities[e.target], 1); + } + } + + self.N = M; + self.topology = newTopology; + self.invMap = newInvMap; + self.nodeCommunities = nodeCommunities; + self.nodeConnectionsWeight = nodeConnectionsWeight; + self.nodeConnectionsCount = nodeConnectionsCount; + } + +} + + +pub struct Modularity { + modularity: f64, + modularityResolution: f64, + // isRandomized: bool, + useWeight: bool, + resolution: f64, + noise: u32, + cc : CommunityCatalog, + pub communityByNode: Vec, +} + +impl Modularity { + + /// + /// resolution: 1.0 More resolution produce less Clusters + /// noise: 0 clusters with number_of_nodes <= noise are considered noise (-1 tag) + /// + pub fn new(resolution: f64, noise: u32) -> Modularity { + Modularity { + modularity: 0.0, + modularityResolution: 0.0, + // isRandomized: false, + useWeight: true, + resolution, + noise, + cc: Default::default(), + communityByNode: Default::default() + } + } + + pub fn execute(&mut self, graph: & G) + -> (f64, f64) where + NI: Idx, + G: Graph + DirectedDegrees + DirectedNeighborsWithValues + Sync { + + let mut structure = CommunityStructure::new(graph, self); + let mut communities : Vec= vec![0; graph.node_count().index()]; + + if graph.node_count().index() > 0 { + let (modularity, modularityResolution) = self.computeModularity(graph, &mut structure, &mut communities); + self.modularity = modularity; + self.modularityResolution = modularityResolution; + } else { + self.modularity = 0.0; + self.modularityResolution = 0.0; + } + // saveValues(comStructure, graph, structure); + self.communityByNode = communities; + + (self.modularity, self.modularityResolution) + } + + fn computeModularity(&mut self, graph: & G, cs : &mut CommunityStructure, communities: &mut Vec) -> (f64, f64) where + NI: Idx, + G: Graph + DirectedDegrees + DirectedNeighborsWithValues + Sync { + + let nodeDegrees = cs.weights.clone(); + let totalWeight = cs.graphWeightSum; + + let mut someChange = true; + while someChange { + someChange = false; + let mut localChange = true; + let mut movedNodes: u32 = 0; + while localChange { + localChange = false; + let start: usize = 0; + // if self.isRandomized { + // let mut rng = thread_rng(); + // start = seq::sample_iter(&mut rng, 1..cs.N, 1).unwrap()[0]; + // } + for step in 0..cs.N { + let i = (step + start) % cs.N; + if let Some(bestCommunity) = self.updateBestCommunity(cs, i, self.resolution) { + if cs.nodeCommunities[i] != bestCommunity { + cs.moveNodeTo(i, bestCommunity, &mut self.cc); + localChange = true; + movedNodes += 1; + } + } + if (step % 10000) == 0 { + println!("Step {} of {} -> moved nodes: {}", step, cs.N, movedNodes); + } + } + someChange = localChange || someChange; + println!("Node Pass Ended -> moved nodes: {}", movedNodes); + } + if someChange { + cs.zoomOut(&mut self.cc); + println!("Zooming Out: {} communities left", cs.N); + } + } + let mut comStructure : Vec= vec![0; graph.node_count().index()]; + let noiseMap = self.fillComStructure(cs, &mut comStructure); + let degreeCount = self.fillDegreeCount(graph, cs, &mut comStructure, & nodeDegrees); + + let computedModularity = self.finalQ(&mut comStructure, & degreeCount, graph, totalWeight, 1.); + let computedModularityResolution = self.finalQ(&mut comStructure, & degreeCount, graph, totalWeight, self.resolution); + + for i in 0..communities.len() { + communities[i] = if noiseMap[i] { -1 } else { comStructure[i] as i32 } + } + // let communities: Vec<&Community> = cs.communities.iter().map(|c| self.cc.get(c).unwrap()).collect(); + // println!("{:?}", comStructure); + + (computedModularity, computedModularityResolution) + } + + fn fillComStructure(&self, cs : & CommunityStructure, comStructure: &mut Vec) -> Vec { + let mut noiseMap: Vec = vec![false; comStructure.len()]; + for (count, com_id) in cs.communities.iter().enumerate() { + let com = self.cc.get(com_id).unwrap(); + for node in & com.nodes { + let hidden_id = cs.invMap.get(node).unwrap(); + let hidden = self.cc.get(hidden_id).unwrap(); + let isNoise = hidden.nodes.len() as u32 <= self.noise; + for nodeInt in & hidden.nodes { + comStructure[* nodeInt] = count; + noiseMap[* nodeInt] = isNoise; + } + } + } + noiseMap + } + + fn fillDegreeCount(&self, graph: & G, cs : & CommunityStructure, + comStructure: & Vec, nodeDegrees: & Vec) -> Vec where + NI: Idx, + G: Graph + DirectedDegrees + DirectedNeighborsWithValues + Sync { + + let mut degreeCount : Vec = vec![0.0; cs.communities.len()]; + let N = graph.node_count().index(); + + (0..N) + .into_iter() + .map(NI::new) + .for_each(|node| { + if self.useWeight { + degreeCount[comStructure[node.index()]] += nodeDegrees[node.index()]; + } else { + degreeCount[comStructure[node.index()]] += graph.out_degree(node).index() as f64; + } + }); + degreeCount + } + + fn updateBestCommunity(&mut self, cs: & CommunityStructure, node_id: usize, currentResolution: f64) -> Option { + let mut best = 0.0; + let mut bestCommunity: Option = None; + // println!("{:?} !!!!{:?}", node_id, cs.nodeConnectionsWeight[node_id]); + for com_id in cs.nodeConnectionsWeight[node_id].keys() { + let qValue = self.q(node_id, com_id, cs, currentResolution); + if qValue > best { + best = qValue; + bestCommunity = Some(*com_id); + } + } + bestCommunity + } + + fn q(& self, + node: usize, + com_id: & CommunityId, + cs: & CommunityStructure, + currentResolution: f64 + ) -> f64 + { + let mut edgesTo: f64 = 0.0; + if let Some(edgesToFloat) = cs.nodeConnectionsWeight[node].get(com_id) { + edgesTo = *edgesToFloat as f64; + } + let weightSum: f64 = self.cc.get(com_id).unwrap().weightSum; + let nodeWeight: f64 = cs.weights[node]; + let mut qValue : f64 = currentResolution * edgesTo - (nodeWeight * weightSum) / (2.0 * cs.graphWeightSum); + + let nodeCommunities_len = self.cc.get(& cs.nodeCommunities[node]).unwrap().size(); + if (cs.nodeCommunities[node] == *com_id) && (nodeCommunities_len > 1) { + qValue = currentResolution * edgesTo - (nodeWeight * (weightSum - nodeWeight)) / (2.0 * cs.graphWeightSum); + } + if (cs.nodeCommunities[node] == *com_id) && (nodeCommunities_len == 1) { + qValue = 0.0; + } + qValue + } + + fn finalQ(&self, comStructure: & Vec, degrees: & Vec, graph: & G, totalWeight: f64, usedResolution: f64) -> f64 where + NI: Idx, + G: Graph + DirectedNeighborsWithValues + Sync { + + let mut res: f64 = 0.0; + let mut internal: Vec = vec![0.0; degrees.len()]; + + let N = graph.node_count().index(); + + (0..N) + .into_iter() + .map(NI::new) + .for_each(|node| { + for Target { target, value } in graph.out_neighbors_with_values(node) { + let neighbor = target.index(); + if node == *target { + continue; + } + + if comStructure[neighbor] == comStructure[node.index() as usize] as usize { + if self.useWeight { + internal[comStructure[neighbor]] += *value as f64; + } else { + internal[comStructure[neighbor]] += 1.0; + } + } + } + }); + + for i in 0..degrees.len() { + internal[i] /= 2.0; + res += usedResolution * (internal[i] / totalWeight) - (degrees[i] / (2.0 * totalWeight)).powi(2); + } + res + } + +} + +// AUX functions + +#[allow(dead_code)] +fn check_weights(cc: &CommunityCatalog, cs: &CommunityStructure) -> bool { + for i in 0..cs.nodeCommunities.len() - 1 { + let com0 = cc.get(&cs.nodeCommunities[i]).unwrap(); + if let Some(w0_1) = com0.connectionsWeight.borrow().get(&cs.nodeCommunities[i+1]) { + let nw0_1 = cs.nodeConnectionsWeight[i].get(&cs.nodeCommunities[i+1]).unwrap(); + // println!("{}: {:?} = {:?} --> {:?}", i, w0_1, nw0_1, f32::abs(w0_1 - nw0_1) < f32::EPSILON ); + if f32::abs(w0_1 - nw0_1) > f32::EPSILON { + return false; + } + } + } + true +} \ No newline at end of file diff --git a/packages/graph-rust/src/page_rank.rs b/packages/graph-rust/src/page_rank.rs new file mode 100644 index 0000000..735ffdd --- /dev/null +++ b/packages/graph-rust/src/page_rank.rs @@ -0,0 +1,153 @@ +use super::graph_builder::{prelude::*, SharedMut}; +use crate::DEFAULT_PARALLELISM; + +use atomic_float::AtomicF64; +use rayon::prelude::*; + +use std::sync::atomic::Ordering; + +const CHUNK_SIZE: usize = 16384; + +#[derive(Copy, Clone, Debug)] +#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))] +pub struct PageRankConfig { + /// The maximum number of page rank iterations. + pub max_iterations: usize, + + /// If the sum of page rank deltas per iteration is + /// below the tolerance value, the computation stop. + pub tolerance: f64, + + /// Imagining a random surfer clicking links, the + /// damping factor defines the probability if the + /// surfer will continue at any step. + pub damping_factor: f32, +} + +impl Default for PageRankConfig { + fn default() -> Self { + Self { + max_iterations: Self::DEFAULT_MAX_ITERATIONS, + tolerance: Self::DEFAULT_TOLERANCE, + damping_factor: Self::DEFAULT_DAMPING_FACTOR, + } + } +} + +impl PageRankConfig { + pub const DEFAULT_MAX_ITERATIONS: usize = 1000; + pub const DEFAULT_TOLERANCE: f64 = 1E-4; + pub const DEFAULT_DAMPING_FACTOR: f32 = 0.85; + + pub fn new(max_iterations: usize, tolerance: f64, damping_factor: f32) -> Self { + Self { + max_iterations, + tolerance, + damping_factor, + } + } +} + +pub fn page_rank(graph: &G, config: PageRankConfig) -> (Vec, usize, f64) +where + NI: Idx, + G: Graph + DirectedDegrees + DirectedNeighbors + Sync, +{ + let PageRankConfig { + max_iterations, + tolerance, + damping_factor, + } = config; + + let node_count = graph.node_count().index(); + let init_score = 1_f32 / node_count as f32; + let base_score = (1.0_f32 - damping_factor) / node_count as f32; + + let mut out_scores = Vec::with_capacity(node_count); + + (0..node_count) + .into_par_iter() + .map(NI::new) + .map(|node| init_score / graph.out_degree(node).index() as f32) + .collect_into_vec(&mut out_scores); + + let mut scores = vec![init_score; node_count]; + + let scores_ptr = SharedMut::new(scores.as_mut_ptr()); + let out_scores_ptr = SharedMut::new(out_scores.as_mut_ptr()); + + let mut iteration = 0; + + loop { + let error = page_rank_iteration( + graph, + base_score, + damping_factor, + &out_scores_ptr, + &scores_ptr, + ); + + iteration += 1; + + if error < tolerance || iteration == max_iterations { + return (scores, iteration, error); + } + } +} + +fn page_rank_iteration( + graph: &G, + base_score: f32, + damping_factor: f32, + out_scores: &SharedMut, + scores: &SharedMut, +) -> f64 +where + NI: Idx, + G: Graph + DirectedDegrees + DirectedNeighbors + Sync, +{ + let next_chunk = Atomic::new(NI::zero()); + let total_error = AtomicF64::new(0_f64); + + rayon::scope(|s| { + let num_threads = DEFAULT_PARALLELISM; + + for _ in 0..num_threads { + s.spawn(|_| { + let mut error = 0_f64; + + loop { + let start = NI::fetch_add(&next_chunk, NI::new(CHUNK_SIZE), Ordering::AcqRel); + if start >= graph.node_count() { + break; + } + + let end = (start + NI::new(CHUNK_SIZE)).min(graph.node_count()); + + for u in start.range(end) { + let incoming_total = graph + .in_neighbors(u) + .map(|v| unsafe { out_scores.add(v.index()).read() }) + .sum::(); + + let old_score = unsafe { scores.add(u.index()).read() }; + let new_score = base_score + damping_factor * incoming_total; + + unsafe { scores.add(u.index()).write(new_score) }; + let diff = (new_score - old_score) as f64; + error += f64::abs(diff); + + unsafe { + out_scores + .add(u.index()) + .write(new_score / graph.out_degree(u).index() as f32) + } + } + } + total_error.fetch_add(error, Ordering::SeqCst); + }); + } + }); + + total_error.load(Ordering::SeqCst) +} diff --git a/packages/graph-rust/src/prelude.rs b/packages/graph-rust/src/prelude.rs new file mode 100644 index 0000000..4c33f9f --- /dev/null +++ b/packages/graph-rust/src/prelude.rs @@ -0,0 +1,6 @@ +pub use crate::page_rank::*; +pub use crate::sssp::*; +pub use crate::louvain::*; +pub use crate::utils::*; + +pub use crate::graph_builder::prelude::*; diff --git a/packages/graph-rust/src/sssp.rs b/packages/graph-rust/src/sssp.rs new file mode 100644 index 0000000..ea95035 --- /dev/null +++ b/packages/graph-rust/src/sssp.rs @@ -0,0 +1,264 @@ +use crate::prelude::*; + +use atomic_float::AtomicF32; +use rayon::prelude::*; + +use std::sync::atomic::{AtomicUsize, Ordering}; + +const INF: f32 = f32::MAX; +const NO_BIN: usize = usize::MAX; +const BIN_SIZE_THRESHOLD: usize = 1000; + +const BATCH_SIZE: usize = 64; + +#[derive(Copy, Clone, Debug)] +#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))] +pub struct DeltaSteppingConfig { + /// The node for which to compute distances to all reachable nodes. + pub start_node: usize, + + /// Defines the "bucket width". A bucket maintains nodes with the + /// same tentative distance to the start node. + pub delta: f32, +} + +impl DeltaSteppingConfig { + pub fn new(start_node: usize, delta: f32) -> Self { + Self { start_node, delta } + } +} + +pub fn delta_stepping(graph: &G, config: DeltaSteppingConfig) -> Vec +where + NI: Idx, + G: Graph + DirectedNeighborsWithValues + Sync, +{ + let DeltaSteppingConfig { start_node, delta } = config; + + let node_count = graph.node_count().index(); + let thread_count = rayon::current_num_threads(); + + let mut distance: Vec = Vec::with_capacity(node_count); + distance.resize_with(node_count, || AtomicF32::new(INF)); + distance[start_node.index()].store(0.0, Ordering::Release); + + let mut frontier = vec![NI::zero(); graph.edge_count().index()]; + frontier[0] = NI::new(start_node); + let frontier_idx = AtomicUsize::new(0); + let mut frontier_len = 1; + + let mut local_bins = Vec::with_capacity(thread_count); + local_bins.resize_with(thread_count, ThreadLocalBins::::new); + + let mut curr_bin = 0; + + while curr_bin != NO_BIN { + frontier_idx.store(0, Ordering::Relaxed); + + let next_bin = local_bins + .par_iter_mut() + .map(|local_bins| { + process_shared_bin( + local_bins, + curr_bin, + graph, + (&frontier, &frontier_idx, frontier_len), + &distance, + delta, + ) + }) + .map(|local_bins| process_local_bins(local_bins, curr_bin, graph, &distance, delta)) + .map(|local_bins| min_non_empty_bin(local_bins, curr_bin)) + .min_by(|x, y| x.cmp(y)) + .unwrap_or(NO_BIN); + + // copy next local bins into shared global bin + frontier_len = frontier_slices(&mut frontier, &local_bins, next_bin) + .par_iter_mut() + .zip(local_bins.par_iter_mut()) + .filter(|(_, local_bins)| local_bins.contains(next_bin)) + .map(|(slice, local_bins)| { + slice.copy_from_slice(local_bins.slice(next_bin)); + local_bins.clear(next_bin); + slice.len() + }) + .sum(); + + curr_bin = next_bin; + } + + distance +} + +fn process_shared_bin<'bins, NI, G>( + bins: &'bins mut ThreadLocalBins, + curr_bin: usize, + graph: &G, + (frontier, frontier_idx, frontier_len): (&[NI], &AtomicUsize, usize), + distance: &[AtomicF32], + delta: f32, +) -> &'bins mut ThreadLocalBins +where + NI: Idx, + G: Graph + DirectedNeighborsWithValues + Sync, +{ + loop { + let offset = frontier_idx.fetch_add(BATCH_SIZE, Ordering::AcqRel); + + if offset >= frontier_len { + break; + } + + let limit = usize::min(offset + BATCH_SIZE, frontier_len); + + for node in frontier[offset..limit].iter() { + if distance[node.index()].load(Ordering::Acquire) >= delta * curr_bin as f32 { + relax_edges(graph, distance, bins, *node, delta); + } + } + } + bins +} + +fn process_local_bins<'bins, NI, G>( + bins: &'bins mut ThreadLocalBins, + curr_bin: usize, + graph: &G, + distance: &[AtomicF32], + delta: f32, +) -> &'bins mut ThreadLocalBins +where + NI: Idx, + G: Graph + DirectedNeighborsWithValues + Sync, +{ + while curr_bin < bins.len() + && !bins.is_empty(curr_bin) + && bins.bin_len(curr_bin) < BIN_SIZE_THRESHOLD + { + let current_bin_copy = bins.clone(curr_bin); + bins.clear(curr_bin); + + for node in current_bin_copy { + relax_edges(graph, distance, bins, node, delta); + } + } + bins +} + +fn min_non_empty_bin(local_bins: &mut ThreadLocalBins, curr_bin: usize) -> usize { + let mut next_local_bin = NO_BIN; + for bin in curr_bin..local_bins.len() { + if !local_bins.is_empty(bin) { + next_local_bin = bin; + break; + } + } + next_local_bin +} + +fn relax_edges( + graph: &G, + distances: &[AtomicF32], + local_bins: &mut ThreadLocalBins, + node: NI, + delta: f32, +) where + NI: Idx, + G: Graph + DirectedNeighborsWithValues + Sync, +{ + for Target { target, value } in graph.out_neighbors_with_values(node) { + let mut old_distance = distances[target.index()].load(Ordering::Acquire); + let new_distance = distances[node.index()].load(Ordering::Acquire) + value; + + while new_distance < old_distance { + match distances[target.index()].compare_exchange_weak( + old_distance, + new_distance, + Ordering::Release, + Ordering::Relaxed, + ) { + Ok(_) => { + let dest_bin = (new_distance / delta) as usize; + if dest_bin >= local_bins.len() { + local_bins.resize(dest_bin + 1); + } + local_bins.push(dest_bin, *target); + break; + } + // CAX failed -> retry with new min distance + Err(min_distance) => old_distance = min_distance, + } + } + } +} + +fn frontier_slices<'a, NI: Idx>( + frontier: &'a mut [NI], + bins: &[ThreadLocalBins], + next_bin: usize, +) -> Vec<&'a mut [NI]> { + let mut slices = Vec::with_capacity(bins.len()); + let mut tail = frontier; + + for local_bins in bins.iter() { + if local_bins.contains(next_bin) { + let (head, remainder) = tail.split_at_mut(local_bins.bin_len(next_bin)); + slices.push(head); + tail = remainder; + } else { + slices.push(&mut []); + } + } + + slices +} + +#[derive(Debug)] +struct ThreadLocalBins { + bins: Vec>, +} + +impl ThreadLocalBins +where + T: Clone, +{ + fn new() -> Self { + Self { bins: vec![vec![]] } + } + + fn contains(&self, bin: usize) -> bool { + self.len() > bin + } + + fn len(&self) -> usize { + self.bins.len() + } + + fn bin_len(&self, bin: usize) -> usize { + self.bins[bin].len() + } + + fn is_empty(&self, bin: usize) -> bool { + self.bins[bin].is_empty() + } + + fn clone(&self, bin: usize) -> Vec { + self.bins[bin].clone() + } + + fn clear(&mut self, bin: usize) { + self.bins[bin].clear(); + } + + fn slice(&self, bin: usize) -> &[T] { + &self.bins[bin] + } + + fn resize(&mut self, new_len: usize) { + self.bins.resize_with(new_len, Vec::new) + } + + fn push(&mut self, bin: usize, val: T) { + self.bins[bin].push(val); + } +} diff --git a/packages/graph-rust/src/utils.rs b/packages/graph-rust/src/utils.rs new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/packages/graph-rust/src/utils.rs @@ -0,0 +1 @@ + diff --git a/packages/graph-wasm/.gitignore b/packages/graph-wasm/.gitignore deleted file mode 100644 index 63392d8..0000000 --- a/packages/graph-wasm/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -target -pkg -pkg-node -pkg-parallel -*.lock \ No newline at end of file diff --git a/packages/graph-wasm/Cargo.lock b/packages/graph-wasm/Cargo.lock index 8a9f789..8441b58 100644 --- a/packages/graph-wasm/Cargo.lock +++ b/packages/graph-wasm/Cargo.lock @@ -3,24 +3,38 @@ version = 3 [[package]] -name = "ahash" -version = "0.8.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2c99f64d1e06488f620f932677e24bc6e2897582980441ae90a671415bd7ec2f" +name = "antv-graph" +version = "0.0.1" dependencies = [ - "cfg-if 1.0.0", - "getrandom", - "once_cell", - "version_check", + "atoi", + "atomic", + "atomic_float", + "byte-slice-cast", + "dashmap", + "delegate", + "fast-float", + "fxhash", + "linereader", + "log", + "memmap2", + "num", + "num-format", + "num_cpus", + "page_size", + "parking_lot", + "rayon", + "serde", + "slotmap", + "thiserror", ] [[package]] name = "antv-graph-wasm" version = "0.0.1" dependencies = [ + "antv-graph", "console_error_panic_hook", "getrandom", - "graph", "js-sys", "serde", "serde-wasm-bindgen", @@ -32,9 +46,9 @@ dependencies = [ [[package]] name = "arrayvec" -version = "0.7.2" +version = "0.7.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8da52d66c7071e2e3fa2a1e5c6d088fec47b593032b254f5e980de8ea54454d6" +checksum = "96d30a06541fbafbc7f82ed10c06164cfbd2c401138f6addd8404629c4b16711" [[package]] name = "atoi" @@ -210,47 +224,6 @@ dependencies = [ "wasm-bindgen", ] -[[package]] -name = "graph" -version = "0.3.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "624b74cfef1d6e08adeac4b5947e8e79351d0a53491b07b9d342701f8e58b68f" -dependencies = [ - "ahash", - "atomic_float", - "graph_builder", - "log", - "nanorand", - "num-format", - "rayon", - "serde", -] - -[[package]] -name = "graph_builder" -version = "0.3.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3bed82dbbf80a458a59ee9fa11b03e194e1429b36d94da2ac59356b44eb926d7" -dependencies = [ - "atoi", - "atomic", - "byte-slice-cast", - "dashmap", - "delegate", - "fast-float", - "fxhash", - "linereader", - "log", - "memmap2", - "num", - "num-format", - "num_cpus", - "page_size", - "parking_lot", - "rayon", - "thiserror", -] - [[package]] name = "hashbrown" version = "0.12.3" @@ -298,9 +271,9 @@ dependencies = [ [[package]] name = "lock_api" -version = "0.4.9" +version = "0.4.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "435011366fe56583b16cf956f9df0095b405b82d76425bc8981c0e22e60ec4df" +checksum = "c1cc9717a20b1bb222f333e6a92fd32f7d8a18ddc5a3191a11af45dcbf4dcd16" dependencies = [ "autocfg", "scopeguard", @@ -342,12 +315,6 @@ version = "0.4.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "8452105ba047068f40ff7093dd1d9da90898e63dd61736462e9cdda6a90ad3c3" -[[package]] -name = "nanorand" -version = "0.7.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6a51313c5820b0b02bd422f4b44776fbf47961755c74ce64afc73bfad10226c3" - [[package]] name = "num" version = "0.4.0" @@ -472,31 +439,31 @@ dependencies = [ [[package]] name = "parking_lot_core" -version = "0.9.7" +version = "0.9.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9069cbb9f99e3a5083476ccb29ceb1de18b9118cafa53e90c9551235de2b9521" +checksum = "93f00c865fe7cabf650081affecd3871070f26767e7b2070a3ffae14c654b447" dependencies = [ "cfg-if 1.0.0", "libc", "redox_syscall", "smallvec", - "windows-sys", + "windows-targets", ] [[package]] name = "proc-macro2" -version = "1.0.59" +version = "1.0.64" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6aeca18b86b413c660b781aa319e4e2648a3e6f9eadc9b47e9038e6fe9f3451b" +checksum = "78803b62cbf1f46fde80d7c0e803111524b9877184cfe7c3033659490ac7a7da" dependencies = [ "unicode-ident", ] [[package]] name = "quote" -version = "1.0.28" +version = "1.0.29" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1b9ab9c7eadfd8df19006f1cf1a4aed13540ed5cbc047010ece5826e10825488" +checksum = "573015e8ab27661678357f27dc26460738fd2b6c86e46f386fde94cb5d913105" dependencies = [ "proc-macro2", ] @@ -525,9 +492,9 @@ dependencies = [ [[package]] name = "redox_syscall" -version = "0.2.16" +version = "0.3.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fb5a58c1855b4b6819d59012155603f0b22ad30cad752600aadfcb695265519a" +checksum = "567664f262709473930a4bf9e51bf2ebf3348f2e748ccc50dea20646858f8f29" dependencies = [ "bitflags", ] @@ -572,7 +539,7 @@ checksum = "8c805777e3930c8883389c602315a24224bcc738b63905ef87cd1420353ea93e" dependencies = [ "proc-macro2", "quote", - "syn 2.0.18", + "syn 2.0.25", ] [[package]] @@ -586,11 +553,17 @@ dependencies = [ "serde", ] +[[package]] +name = "slotmap" +version = "0.4.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6bf34684c5767b87de9119790e92e9a1d60056be2ceeaf16a8e6ef13082aeab1" + [[package]] name = "smallvec" -version = "1.10.0" +version = "1.11.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a507befe795404456341dfab10cef66ead4c041f62b8b11bbb92bffe5d0953e0" +checksum = "62bb4feee49fdd9f707ef802e22365a35de4b7b299de4763d44bfea899442ff9" [[package]] name = "spmc" @@ -611,9 +584,9 @@ dependencies = [ [[package]] name = "syn" -version = "2.0.18" +version = "2.0.25" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "32d41677bcbe24c20c52e7c70b0d8db04134c5d1066bf98662e2871ad200ea3e" +checksum = "15e3fc8c0c74267e2df136e5e5fb656a464158aa57624053375eb9c8c6e25ae2" dependencies = [ "proc-macro2", "quote", @@ -622,22 +595,22 @@ dependencies = [ [[package]] name = "thiserror" -version = "1.0.40" +version = "1.0.43" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "978c9a314bd8dc99be594bc3c175faaa9794be04a5a5e153caba6915336cebac" +checksum = "a35fc5b8971143ca348fa6df4f024d4d55264f3468c71ad1c2f365b0a4d58c42" dependencies = [ "thiserror-impl", ] [[package]] name = "thiserror-impl" -version = "1.0.40" +version = "1.0.43" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f9456a42c5b0d803c8cd86e73dd7cc9edd429499f37a3550d286d5e86720569f" +checksum = "463fe12d7993d3b327787537ce8dd4dfa058de32fc2b195ef3cde03dc4771e8f" dependencies = [ "proc-macro2", "quote", - "syn 2.0.18", + "syn 2.0.25", ] [[package]] @@ -646,12 +619,6 @@ version = "1.0.9" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b15811caf2415fb889178633e7724bad2509101cde276048e013b9def5e51fa0" -[[package]] -name = "version_check" -version = "0.9.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "49874b5167b65d7193b8aba1567f5c7d93d001cafc34600cee003eda787e483f" - [[package]] name = "wasi" version = "0.11.0+wasi-snapshot-preview1" @@ -681,7 +648,7 @@ dependencies = [ "once_cell", "proc-macro2", "quote", - "syn 2.0.18", + "syn 2.0.25", "wasm-bindgen-shared", ] @@ -703,7 +670,7 @@ checksum = "e128beba882dd1eb6200e1dc92ae6c5dbaa4311aa7bb211ca035779e5efc39f8" dependencies = [ "proc-macro2", "quote", - "syn 2.0.18", + "syn 2.0.25", "wasm-bindgen-backend", "wasm-bindgen-shared", ] @@ -760,20 +727,11 @@ version = "0.4.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "712e227841d057c1ee1cd2fb22fa7e5a5461ae8e48fa2ca79ec42cfc1931183f" -[[package]] -name = "windows-sys" -version = "0.45.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "75283be5efb2831d37ea142365f009c02ec203cd29a3ebecbc093d52315b66d0" -dependencies = [ - "windows-targets", -] - [[package]] name = "windows-targets" -version = "0.42.2" +version = "0.48.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8e5180c00cd44c9b1c88adb3693291f1cd93605ded80c250a75d472756b4d071" +checksum = "05d4b17490f70499f20b9e791dcf6a299785ce8af4d709018206dc5b4953e95f" dependencies = [ "windows_aarch64_gnullvm", "windows_aarch64_msvc", @@ -786,42 +744,42 @@ dependencies = [ [[package]] name = "windows_aarch64_gnullvm" -version = "0.42.2" +version = "0.48.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "597a5118570b68bc08d8d59125332c54f1ba9d9adeedeef5b99b02ba2b0698f8" +checksum = "91ae572e1b79dba883e0d315474df7305d12f569b400fcf90581b06062f7e1bc" [[package]] name = "windows_aarch64_msvc" -version = "0.42.2" +version = "0.48.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e08e8864a60f06ef0d0ff4ba04124db8b0fb3be5776a5cd47641e942e58c4d43" +checksum = "b2ef27e0d7bdfcfc7b868b317c1d32c641a6fe4629c171b8928c7b08d98d7cf3" [[package]] name = "windows_i686_gnu" -version = "0.42.2" +version = "0.48.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c61d927d8da41da96a81f029489353e68739737d3beca43145c8afec9a31a84f" +checksum = "622a1962a7db830d6fd0a69683c80a18fda201879f0f447f065a3b7467daa241" [[package]] name = "windows_i686_msvc" -version = "0.42.2" +version = "0.48.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "44d840b6ec649f480a41c8d80f9c65108b92d89345dd94027bfe06ac444d1060" +checksum = "4542c6e364ce21bf45d69fdd2a8e455fa38d316158cfd43b3ac1c5b1b19f8e00" [[package]] name = "windows_x86_64_gnu" -version = "0.42.2" +version = "0.48.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8de912b8b8feb55c064867cf047dda097f92d51efad5b491dfb98f6bbb70cb36" +checksum = "ca2b8a661f7628cbd23440e50b05d705db3686f894fc9580820623656af974b1" [[package]] name = "windows_x86_64_gnullvm" -version = "0.42.2" +version = "0.48.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "26d41b46a36d453748aedef1486d5c7a85db22e56aff34643984ea85514e94a3" +checksum = "7896dbc1f41e08872e9d5e8f8baa8fdd2677f29468c4e156210174edc7f7b953" [[package]] name = "windows_x86_64_msvc" -version = "0.42.2" +version = "0.48.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9aec5da331524158c6d1a4ac0ab1541149c0b9505fde06423b02f5ef0106b9f0" +checksum = "1a515f5799fe4961cb532f983ce2b23082366b898e52ffbce459c86f67c8378a" diff --git a/packages/graph-wasm/Cargo.toml b/packages/graph-wasm/Cargo.toml index c0166f0..ebc25d7 100644 --- a/packages/graph-wasm/Cargo.toml +++ b/packages/graph-wasm/Cargo.toml @@ -24,7 +24,7 @@ path = "rust-src/lib.rs" [dependencies] getrandom = { version = "0.2", features = ["js"] } -graph = { version = "0.3.0", features = ["serde"] } +antv-graph = { path = "../graph-rust/", features = ["serde"] } console_error_panic_hook = "0.1" js-sys = "0.3.51" serde-wasm-bindgen = "0.5" diff --git a/packages/graph-wasm/README.md b/packages/graph-wasm/README.md index e69de29..0d90557 100644 --- a/packages/graph-wasm/README.md +++ b/packages/graph-wasm/README.md @@ -0,0 +1,119 @@ +# @antv/graph-wasm + +A WASM binding of `@antv/graph-rust`. We used [wasm-bindgen-rayon](https://github.com/GoogleChromeLabs/wasm-bindgen-rayon) to implement data parallelism with WebWorkers. + +- [Use with Webpack](#webpack) +- [Use with Vite](#vite) + +![pagerank](https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*klB7TKFwbskAAAAAAAAAAAAADmJ7AQ/original) + +## Usage + +Since [cross origin workers are blocked](https://stackoverflow.com/questions/58098143/why-are-cross-origin-workers-blocked-and-why-is-the-workaround-ok/60015898#60015898), we do not recommand the UMD way of using it for now. You can opt to ESM usage with bundler such as [Webpack](#webpack) or [Vite](#vite). + +### ESM + +```js +import { initThreads, supportsThreads } from "@antv/graph-wasm"; +``` + +Since [Not all browsers](https://webassembly.org/roadmap/) support WebAssembly threads yet, we need to use feature detection to choose the right one on the JavaScript side. + +```js +const supported = await supportsThreads(); // `true` means we can use multithreads now! +const threads = await initThreads(supported); +``` + +```js +import { Graph } from "@antv/graphlib"; + +const results = await threads.pageRank( + graph, + { + tolerance: 1e-5, + alpha: 0.85, + maxIterations: 1000 + } +); +``` + +### Use WASM with multithreads + +First of all, in order to use SharedArrayBuffer on the Web, you need to enable [cross-origin isolation policies](https://web.dev/coop-coep/). Check out the linked article for details. + +To opt in to a cross-origin isolated state, you need to send the following HTTP headers on the main document: + +``` +Cross-Origin-Embedder-Policy: require-corp +Cross-Origin-Opener-Policy: same-origin +``` + +If you can't control the server, try this hacky workaround which implemented with ServiceWorker: https://github.com/orgs/community/discussions/13309#discussioncomment-3844940. Here's an example on [Stackblitz](https://stackblitz.com/edit/github-wpncwj-fxmffg?file=src/index.js). + +### Webpack + +Webpack has good support for Webworker, here's an example on [Stackblitz](https://stackblitz.com/edit/github-wpncwj?file=src/index.js). We use [statikk](https://www.npmjs.com/package/statikk) as static server in this example since it has a good support of cross-origin isolation headers. For more information, please refer to [Use WASM with multithreads](#use-wasm-with-multithreads). + +### Vite + +Vite also provides [worker options](https://vitejs.dev/config/worker-options.html) in its config. To let Vite [process URL correctly](https://vitejs.dev/guide/dep-pre-bundling.html#customizing-the-behavior) when creating WebWorker in third-party packages, we need to add the package to `optimizeDeps.exclude`: + +```js +// vite.config.js +optimizeDeps: { + exclude: ['@antv/graph-wasm'], +}, +``` + +To enable COOP & COEP headers, we can set them with `plugins`: + +```js +// vite.config.js +plugins: [ + { + name: 'isolation', + configureServer(server) { + // @see https://gist.github.com/mizchi/afcc5cf233c9e6943720fde4b4579a2b + server.middlewares.use((_req, res, next) => { + res.setHeader('Cross-Origin-Opener-Policy', 'same-origin'); + res.setHeader('Cross-Origin-Embedder-Policy', 'require-corp'); + next(); + }); + }, + }, +], +``` + +Here's a complete example on [Stackblitz](https://stackblitz.com/edit/vite-6b9ga6?file=README.md). + +If you can't control the server, try this hacky workaround which implemented with ServiceWorker: https://github.com/orgs/community/discussions/13309#discussioncomment-3844940 + +## API Reference + +### PageRank + +* `tolerance` Set the tolerance the approximation, this parameter should be a small magnitude value. The lower the tolerance the better the approximation. The default value is 0.0001. +* `alpha` The damping factor alpha represents the probability to follow an outgoing edge, standard value is 0.85. +* `maxIterations` Set the maximum number of iterations. The default value is 1000. + +### SSSP + +## Build + +Install [wasm-pack](https://rustwasm.github.io/wasm-pack/installer/) first. + +Then run the command `npm run build`, the compiled package will be outputted under the `/dist` directory. + +```bash +$ npm run build +``` + +## Publish + +```bash +$ npm publish +``` + +## License + +The scripts and documentation in this project are released under the [MIT License](LICENSE). diff --git a/packages/graph-wasm/package.json b/packages/graph-wasm/package.json index 9ff17cd..383fcee 100644 --- a/packages/graph-wasm/package.json +++ b/packages/graph-wasm/package.json @@ -38,14 +38,5 @@ "comlink": "^4.3.1", "wasm-feature-detect": "^1.2.10", "tslib": "^2.5.0" - }, - "devDependencies": { - "cross-env": "^7.0.3", - "npm-run-all": "^4.1.5", - "rimraf": "^3.0.2", - "ts-loader": "^7.0.3", - "typescript": "^4.0.3", - "webpack": "^5.38.1", - "webpack-cli": "^5.0.2" } } diff --git a/packages/graph-wasm/rust-src/lib.rs b/packages/graph-wasm/rust-src/lib.rs index c08f7ae..d79008f 100644 --- a/packages/graph-wasm/rust-src/lib.rs +++ b/packages/graph-wasm/rust-src/lib.rs @@ -1,6 +1,8 @@ -use graph::prelude::*; +use std::sync::atomic::Ordering; +use antv_graph::prelude::*; use js_sys::Array; use wasm_bindgen::prelude::*; +use serde::{Deserialize, Serialize}; #[cfg(feature = "parallel")] pub use wasm_bindgen_rayon::init_thread_pool; @@ -14,35 +16,23 @@ pub fn start() { console_error_panic_hook::set_once(); } +#[derive(Serialize, Deserialize)] +pub struct PageRankParams { + pub max_iterations: usize, + pub tolerance: f64, + pub damping_factor: f32, + pub edgelist: Vec<(usize, usize)>, +} + #[wasm_bindgen(js_name = "pageRank")] pub fn page_rank(val: JsValue) -> Array { - let options: PageRankConfig = serde_wasm_bindgen::from_value(val).unwrap(); + let options: PageRankParams = serde_wasm_bindgen::from_value(val).unwrap(); let graph: DirectedCsrGraph = GraphBuilder::new() - .edges(vec![ - (1, 2), // B->C - (2, 1), // C->B - (4, 0), // D->A - (4, 1), // D->B - (5, 4), // E->D - (5, 1), // E->B - (5, 6), // E->F - (6, 1), // F->B - (6, 5), // F->E - (7, 1), // G->B - (7, 5), // F->E - (8, 1), // G->B - (8, 5), // G->E - (9, 1), // H->B - (9, 5), // H->E - (10, 1), // I->B - (10, 5), // I->E - (11, 5), // J->B - (12, 5), // K->B - ]) + .edges(options.edgelist) .build(); - let (ranks, iterations, _) = graph::prelude::page_rank( + let (ranks, _, _) = antv_graph::prelude::page_rank( &graph, PageRankConfig::new( options.max_iterations, @@ -54,3 +44,37 @@ pub fn page_rank(val: JsValue) -> Array { // @see https://stackoverflow.com/a/58996628 ranks.into_iter().map(JsValue::from).collect() } + +#[derive(Serialize, Deserialize)] +pub struct SSSPParams { + pub start_node: usize, + pub delta: f32, + pub edgelist: Vec<(usize, usize, f32)>, +} + +#[wasm_bindgen(js_name = "sssp")] +pub fn sssp(val: JsValue) -> Array { + let options: SSSPParams = serde_wasm_bindgen::from_value(val).unwrap(); + + let graph: DirectedCsrGraph = GraphBuilder::new().edges_with_values(options.edgelist).build(); + let config = DeltaSteppingConfig::new(options.start_node, options.delta); + let result: Vec = antv_graph::prelude::delta_stepping(&graph, config).into_iter() + .map(|d| d.load(Ordering::Relaxed)) + .collect(); + result.into_iter().map(JsValue::from).collect() +} + + +#[derive(Serialize, Deserialize)] +pub struct LouvainParams { + pub edgelist: Vec<(usize, usize, f32)>, +} +#[wasm_bindgen(js_name = "louvain")] +pub fn louvain(val: JsValue) -> Array { + let options: LouvainParams = serde_wasm_bindgen::from_value(val).unwrap(); + let graph: DirectedCsrGraph = GraphBuilder::new().edges_with_values(options.edgelist).build(); + + let mut modularity = Modularity::new(1.0, 1); + let results = modularity.execute(& graph); + vec![results.0, results.1].into_iter().map(JsValue::from).collect() +} \ No newline at end of file diff --git a/packages/graph-wasm/src/interface.ts b/packages/graph-wasm/src/interface.ts index 6ce1c7c..8974be0 100644 --- a/packages/graph-wasm/src/interface.ts +++ b/packages/graph-wasm/src/interface.ts @@ -1,3 +1,28 @@ +export interface PageRankParams { + maxIterations?: number; + alpha?: number; + tolerance?: number; + /** + * [source, target] + */ + edgelist: [number, number][]; +} + +export interface SSSPParams { + startNode?: number; + delta?: number; + /** + * [source, target, weight] + */ + edgelist: [number, number, number][]; +} + +export interface LouvainParams { + edgelist: [number, number, number][]; +} + export interface Threads { - page_rank: (options: any) => Promise<{ ranks: number[] }>; + pageRank: (options: PageRankParams) => Promise; + sssp: (options: SSSPParams) => Promise; + louvain: (options: LouvainParams) => Promise; } diff --git a/packages/graph-wasm/src/wasm-worker.js b/packages/graph-wasm/src/wasm-worker.js index ff2fbb2..5668e05 100644 --- a/packages/graph-wasm/src/wasm-worker.js +++ b/packages/graph-wasm/src/wasm-worker.js @@ -1,42 +1,65 @@ -import * as Comlink from "comlink"; +import * as Comlink from 'comlink'; -const DEFAULT_PAGE_RANK_OPTIONS = { - max_iterations: 10, - tolerance: 0.0001, - damping_factor: 0.85, +const wrapTransferPageRank = pageRank => { + return options => { + const params = { + max_iterations: options.maxIterations || 1000, + tolerance: options.tolerance || 0.0001, + damping_factor: options.alpha || 0.85, + edgelist: options.edgelist + }; + + const ranks = pageRank(params); + + return Comlink.transfer(ranks, []); + }; }; -const wrapTransfer = (page_rank) => { - return (options) => { - const ranks = page_rank({ - ...DEFAULT_PAGE_RANK_OPTIONS, - ...options, - }); +const wrapTransferSSSP = sssp => { + return options => { + const params = { + start_node: options.startNode || 0, + delta: options.delta || 1, + edgelist: options.edgelist + }; + + const ranks = sssp(params); - return { - // Little perf boost to transfer data to the main thread w/o copying. - ranks: Comlink.transfer(ranks, [ranks]), + return Comlink.transfer(ranks, []); + }; +}; + +const wrapTransferLouvain = louvain => { + return options => { + const params = { + edgelist: options.edgelist }; + + const ranks = louvain(params); + + return Comlink.transfer(ranks, []); }; }; // Wrap wasm-bindgen exports (the `generate` function) to add time measurement. -function wrapExports({ page_rank }) { +function wrapExports({ pageRank, sssp, louvain }) { return { - page_rank: wrapTransfer(page_rank), + pageRank: wrapTransferPageRank(pageRank), + sssp: wrapTransferSSSP(sssp), + louvain: wrapTransferLouvain(louvain) }; } async function initHandlers(useMultiThread) { if (useMultiThread) { // @ts-ignore - const multiThread = await import("../pkg-parallel/antv_graph_wasm.js"); + const multiThread = await import('../pkg-parallel/antv_graph_wasm.js'); await multiThread.default(); await multiThread.initThreadPool(navigator.hardwareConcurrency); return Comlink.proxy(wrapExports(multiThread)); } else { // @ts-ignore - const singleThread = await import("../pkg/antv_graph_wasm.js"); + const singleThread = await import('../pkg/antv_graph_wasm.js'); await singleThread.default(); return Comlink.proxy(wrapExports(singleThread)); } diff --git a/packages/graph-wasm/tsconfig.json b/packages/graph-wasm/tsconfig.json new file mode 100644 index 0000000..6663048 --- /dev/null +++ b/packages/graph-wasm/tsconfig.json @@ -0,0 +1,25 @@ +{ + "extends": "../../tsconfig.json", + "compilerOptions": { + "rootDir": "./src", + "composite": true, + "module": "esnext", + "declaration": true, + "sourceMap": true, + "target": "es5", + "outDir": "lib", + "jsx": "react", + "importHelpers": true, + "moduleResolution": "node", + "allowSyntheticDefaultImports": true, + "resolveJsonModule": true, + "esModuleInterop": true, + "lib": ["esnext", "dom"], + "experimentalDecorators": false /* Enables experimental support for ES7 decorators. */, + // "emitDecoratorMetadata": true, /* Enables experimental support for emitting type metadata for decorators. */ + "downlevelIteration": true, + "skipLibCheck": true + }, + "include": ["src/**/*"], + "exclude": ["src/**/*.spec.ts"] +} diff --git a/packages/graph/.prettierrc.js b/packages/graph/.prettierrc.js deleted file mode 100644 index 7b597d7..0000000 --- a/packages/graph/.prettierrc.js +++ /dev/null @@ -1,5 +0,0 @@ -const fabric = require('@umijs/fabric'); - -module.exports = { - ...fabric.prettier, -}; diff --git a/packages/graph/jest.config.js b/packages/graph/jest.config.js deleted file mode 100644 index 74a1e59..0000000 --- a/packages/graph/jest.config.js +++ /dev/null @@ -1,15 +0,0 @@ -module.exports = { - runner: 'jest-electron/runner', - testEnvironment: 'jest-electron/environment', - preset: 'ts-jest', - collectCoverage: false, - collectCoverageFrom: ['src/**/*.{ts,js}', '!**/node_modules/**', '!**/vendor/**'], - testRegex: '/tests/.*-spec\\.ts?$', - moduleDirectories: ['node_modules', 'src'], - moduleFileExtensions: ['js', 'ts', 'json'], - globals: { - 'ts-jest': { - diagnostics: false, - }, - }, -}; diff --git a/packages/graph/package.json b/packages/graph/package.json index 6c1754f..ea5fecb 100644 --- a/packages/graph/package.json +++ b/packages/graph/package.json @@ -1,12 +1,11 @@ { - "name": "@antv/algorithm", - "version": "0.1.25", + "name": "@antv/graph", + "version": "0.0.1", "description": "graph algorithm", "keywords": [ "graph", "algorithm", - "antv", - "G6" + "antv" ], "files": [ "package.json", @@ -15,26 +14,19 @@ "LICENSE", "README.md" ], + "sideEffects": false, "main": "dist/index.min.js", "module": "lib/index.js", "types": "lib/index.d.ts", "unpkg": "dist/index.min.js", "scripts": { + "clean": "rimraf dist lib", + "dev": "webpack --config webpack.dev.config.js --mode development", "build": "npm run clean && npm run build:esm && npm run build:umd", + "build:ci": "npm run build", "build:esm": "tsc", "build:umd": "webpack --config webpack.config.js --mode production", - "dev:umd": "webpack --config webpack.dev.config.js --mode development", - "ci": "npm run build && npm run coverage", - "clean": "rimraf lib dist", - "coverage": "jest --coverage", - "lint": "eslint --ext .js,.jsx,.ts,.tsx --format=pretty \"./\"", - "lint:src": "eslint --ext .ts --format=pretty \"./src\"", - "prettier": "prettier -c --write \"**/*\"", - "test": "npm run build:umd && jest", - "test-live": "npm run build:umd && DEBUG_MODE=1 jest --watch ./tests/unit/louvain-spec.ts", - "test-live:async": "npm run build:umd && DEBUG_MODE=1 jest --watch ./tests/unit/louvain-async-spec.ts", - "lint-staged:js": "eslint --ext .js,.jsx,.ts,.tsx", - "cdn": "antv-bin upload -n @antv/algorithm" + "publish:alpha": "npm publish --tag alpha" }, "homepage": "https://g6.antv.vision", "bugs": { @@ -47,25 +39,14 @@ "license": "MIT", "author": "https://github.com/orgs/antvis/people", "devDependencies": { - "@babel/core": "^7.12.10", - "@babel/plugin-proposal-class-properties": "^7.12.1", - "@babel/preset-env": "^7.12.7", - "@babel/preset-typescript": "^7.12.7", - "@types/jest": "^26.0.18", - "@umijs/fabric": "^2.5.6", - "babel-loader": "^8.2.2", - "jest": "^26.6.3", - "rimraf": "^3.0.2", - "ts-jest": "^26.4.4", - "ts-loader": "^8.0.14", - "tslint": "^6.1.3", - "typescript": "^4.1.3", - "webpack": "^5.38.1", - "webpack-cli": "^5.0.2", - "worker-loader": "^3.0.7" + "@babel/core": "^7.7.7", + "@babel/plugin-proposal-class-properties": "^7.1.0", + "@types/d3-force": "^3.0.4", + "babel-loader": "^8.0.6" }, "dependencies": { "@antv/util": "^2.0.13", + "@antv/graphlib": "^2.0.0", "tslib": "^2.5.2" } } diff --git a/packages/graph/src/adjMatrix.ts b/packages/graph/src/adjMatrix.ts new file mode 100644 index 0000000..6e607ab --- /dev/null +++ b/packages/graph/src/adjMatrix.ts @@ -0,0 +1,29 @@ +import { Graph, Matrix } from "./types"; + +export function graph2AdjacencyMatrix(graph: Graph, directed?: boolean): Matrix[] { + const matrix: Matrix[] = []; + // map node with index in data.nodes + const nodeMap: { + [key: string]: number; + } = {}; + + graph.getAllNodes().forEach((node, i) => { + nodeMap[node.id] = i; + const row: number[] = []; + matrix.push(row); + }); + + + graph.getAllEdges().forEach((edge) => { + const { source, target } = edge; + const sIndex = nodeMap[source as string]; + const tIndex = nodeMap[target as string]; + if ((!sIndex && sIndex !== 0) || (!tIndex && tIndex !== 0)) return; + matrix[sIndex][tIndex] = 1; + if (!directed) { + matrix[tIndex][sIndex] = 1; + } + }); + + return matrix; +} \ No newline at end of file diff --git a/packages/graph/src/adjacent-matrix.ts b/packages/graph/src/adjacent-matrix.ts deleted file mode 100644 index 6c484b4..0000000 --- a/packages/graph/src/adjacent-matrix.ts +++ /dev/null @@ -1,38 +0,0 @@ -import { GraphData, Matrix } from "./types"; - -const adjMatrix = (graphData: GraphData, directed?: boolean): Matrix[] => { - const { nodes, edges } = graphData; - const matrix: Matrix[] = []; - // map node with index in data.nodes - const nodeMap: { - [key: string]: number; - } = {}; - - if (!nodes) { - throw new Error("invalid nodes data!"); - } - - if (nodes) { - nodes.forEach((node, i) => { - nodeMap[node.id] = i; - const row: number[] = []; - matrix.push(row); - }); - } - - if (edges) { - edges.forEach((edge) => { - const { source, target } = edge; - const sIndex = nodeMap[source as string]; - const tIndex = nodeMap[target as string]; - if ((!sIndex && sIndex !== 0) || (!tIndex && tIndex !== 0)) return; - matrix[sIndex][tIndex] = 1; - if (!directed) { - matrix[tIndex][sIndex] = 1; - } - }); - } - return matrix; -}; - -export default adjMatrix; diff --git a/packages/graph/src/asyncIndex.ts b/packages/graph/src/asyncIndex.ts deleted file mode 100644 index 1ee629b..0000000 --- a/packages/graph/src/asyncIndex.ts +++ /dev/null @@ -1,69 +0,0 @@ -import { - getAdjMatrixAsync, - connectedComponentAsync, - getDegreeAsync, - getInDegreeAsync, - getOutDegreeAsync, - detectCycleAsync, - detectAllCyclesAsync, - detectAllDirectedCycleAsync, - detectAllUndirectedCycleAsync, - dijkstraAsync, - findAllPathAsync, - findShortestPathAsync, - floydWarshallAsync, - labelPropagationAsync, - louvainAsync, - minimumSpanningTreeAsync, - pageRankAsync, - getNeighborsAsync, - GADDIAsync, -} from './workers/index'; - -const detectDirectedCycleAsync = detectCycleAsync; - -export { - getAdjMatrixAsync, - connectedComponentAsync, - getDegreeAsync, - getInDegreeAsync, - getOutDegreeAsync, - detectCycleAsync, - detectDirectedCycleAsync, - detectAllCyclesAsync, - detectAllDirectedCycleAsync, - detectAllUndirectedCycleAsync, - dijkstraAsync, - findAllPathAsync, - findShortestPathAsync, - floydWarshallAsync, - labelPropagationAsync, - louvainAsync, - minimumSpanningTreeAsync, - pageRankAsync, - getNeighborsAsync, - GADDIAsync, -}; - -export default { - getAdjMatrixAsync, - connectedComponentAsync, - getDegreeAsync, - getInDegreeAsync, - getOutDegreeAsync, - detectCycleAsync, - detectDirectedCycleAsync, - detectAllCyclesAsync, - detectAllDirectedCycleAsync, - detectAllUndirectedCycleAsync, - dijkstraAsync, - findAllPathAsync, - findShortestPathAsync, - floydWarshallAsync, - labelPropagationAsync, - louvainAsync, - minimumSpanningTreeAsync, - pageRankAsync, - getNeighborsAsync, - GADDIAsync, -}; \ No newline at end of file diff --git a/packages/graph/src/bfs.ts b/packages/graph/src/bfs.ts deleted file mode 100644 index 69df6d2..0000000 --- a/packages/graph/src/bfs.ts +++ /dev/null @@ -1,89 +0,0 @@ -import Queue from './structs/queue' -import { GraphData, IAlgorithmCallbacks } from './types'; -import { getNeighbors } from './util'; - -/** - * - * @param callbacks - * allowTraversal: 确定 BFS 是否从顶点沿着边遍历到其邻居，默认情况下，同一个节点只能遍历一次 - * enterNode: 当 BFS 访问某个节点时调用 - * leaveNode: 当 BFS 访问访问结束某个节点时调用 - */ -function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks) { - const initiatedCallback = callbacks; - - const stubCallback = () => {}; - - const allowTraversalCallback = (() => { - const seen = {}; - return ({ next }) => { - const id = next; - if (!seen[id]) { - seen[id] = true; - return true; - } - return false; - }; - })(); - - initiatedCallback.allowTraversal = callbacks.allowTraversal || allowTraversalCallback; - initiatedCallback.enter = callbacks.enter || stubCallback; - initiatedCallback.leave = callbacks.leave || stubCallback; - - return initiatedCallback; -} - -/** - * 广度优先遍历图 - * @param graph Graph 图实例 - * @param startNode 开始遍历的节点 - * @param originalCallbacks 回调 - */ -const breadthFirstSearch = ( - graphData: GraphData, - startNodeId: string, - originalCallbacks?: IAlgorithmCallbacks, - directed: boolean = true -) => { - const callbacks = initCallbacks(originalCallbacks); - const nodeQueue = new Queue(); - - const { edges = [] } = graphData - - // 初始化队列元素 - nodeQueue.enqueue(startNodeId); - - let previousNode = ''; - - // 遍历队列中的所有顶点 - while (!nodeQueue.isEmpty()) { - const currentNode: string = nodeQueue.dequeue(); - callbacks.enter({ - current: currentNode, - previous: previousNode, - }); - - // 将所有邻居添加到队列中以便遍历 - getNeighbors(currentNode, edges, directed ? 'target' : undefined).forEach((nextNode) => { - if ( - callbacks.allowTraversal({ - previous: previousNode, - current: currentNode, - next: nextNode, - }) - ) { - nodeQueue.enqueue(nextNode); - } - }); - - callbacks.leave({ - current: currentNode, - previous: previousNode, - }); - - // 下一次循环之前存储当前顶点 - previousNode = currentNode; - } -}; - -export default breadthFirstSearch; diff --git a/packages/graph/src/connected-component.ts b/packages/graph/src/connected-component.ts deleted file mode 100644 index 4db77be..0000000 --- a/packages/graph/src/connected-component.ts +++ /dev/null @@ -1,113 +0,0 @@ -import { GraphData, NodeConfig } from "./types"; -import { getNeighbors } from "./util"; - -/** - * Generate all connected components for an undirected graph - * @param graph - */ -export const detectConnectedComponents = (graphData: GraphData): NodeConfig[][] => { - const { nodes = [], edges = [] } = graphData - const allComponents: NodeConfig[][] = []; - const visited = {}; - const nodeStack: NodeConfig[] = []; - - const getComponent = (node: NodeConfig) => { - nodeStack.push(node); - visited[node.id] = true; - const neighbors = getNeighbors(node.id, edges); - for (let i = 0; i < neighbors.length; ++i) { - const neighbor = neighbors[i]; - if (!visited[neighbor]) { - const targetNode = nodes.filter(node => node.id === neighbor) - if (targetNode.length > 0) { - getComponent(targetNode[0]); - } - } - } - }; - - for (let i = 0; i < nodes.length; i++) { - const node = nodes[i]; - if (!visited[node.id]) { - // 对于无向图进行dfs遍历，每一次调用后都得到一个连通分量 - getComponent(node); - const component = []; - while (nodeStack.length > 0) { - component.push(nodeStack.pop()); - } - allComponents.push(component); - } - } - return allComponents; -} - -/** - * Tarjan's Algorithm 复杂度 O(|V|+|E|) - * For directed graph only - * a directed graph is said to be strongly connected if "every vertex is reachable from every other vertex". - * refer: http://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithm - * @param graph - * @return a list of strongly connected components - */ -export const detectStrongConnectComponents = (graphData: GraphData): NodeConfig[][] => { - const { nodes = [], edges = [] } = graphData - const nodeStack: NodeConfig[] = []; - const inStack = {}; // 辅助判断是否已经在stack中，减少查找开销 - const indices = {}; - const lowLink = {}; - const allComponents: NodeConfig[][] = []; - let index = 0; - - const getComponent = (node: NodeConfig) => { - // Set the depth index for v to the smallest unused index - indices[node.id] = index; - lowLink[node.id] = index; - index += 1; - nodeStack.push(node); - inStack[node.id] = true; - - // 考虑每个邻接点 - const neighbors = getNeighbors(node.id, edges, 'target').filter((n) => nodes.map(node => node.id).indexOf(n) > -1); - for (let i = 0; i < neighbors.length; i++) { - const targetNodeID = neighbors[i]; - if (!indices[targetNodeID] && indices[targetNodeID] !== 0) { - const targetNode = nodes.filter(node => node.id === targetNodeID) - if (targetNode.length > 0) { - getComponent(targetNode[0]); - } - // tree edge - lowLink[node.id] = Math.min(lowLink[node.id], lowLink[targetNodeID]); - } else if (inStack[targetNodeID]) { - // back edge, target node is in the current SCC - lowLink[node.id] = Math.min(lowLink[node.id], indices[targetNodeID]); - } - } - - // If node is a root node, generate an SCC - if (lowLink[node.id] === indices[node.id]) { - const component = []; - while (nodeStack.length > 0) { - const tmpNode = nodeStack.pop(); - inStack[tmpNode.id] = false; - component.push(tmpNode); - if (tmpNode === node) break; - } - if (component.length > 0) { - allComponents.push(component); - } - } - }; - - for (const node of nodes) { - if (!indices[node.id] && indices[node.id] !== 0) { - getComponent(node); - } - } - - return allComponents; -} - -export default function getConnectedComponents(graphData: GraphData, directed?: boolean): NodeConfig[][] { - if (directed) return detectStrongConnectComponents(graphData); - return detectConnectedComponents(graphData); -} diff --git a/packages/graph/src/constants/time.ts b/packages/graph/src/constants/time.ts deleted file mode 100644 index 383bcae..0000000 --- a/packages/graph/src/constants/time.ts +++ /dev/null @@ -1,2 +0,0 @@ -export const secondReg = /^(\d{1,4})(-|\/)(\d{1,2})\2(\d{1,2})$/; -export const dateReg = /^(\d{1,4})(-|\/)(\d{1,2})\2(\d{1,2}) (\d{1,2}):(\d{1,2}):(\d{1,2})$/; diff --git a/packages/graph/src/cosine-similarity.ts b/packages/graph/src/cosine-similarity.ts deleted file mode 100644 index 2b6e25c..0000000 --- a/packages/graph/src/cosine-similarity.ts +++ /dev/null @@ -1,27 +0,0 @@ -import Vector from './utils/vector'; -/** - * cosine-similarity算法 计算余弦相似度 - * @param item 元素 - * @param targetItem 目标元素 - */ -const cosineSimilarity = ( - item: number[], - targetItem: number[], -): number => { - // 目标元素向量 - const targetItemVector = new Vector(targetItem); - // 目标元素向量的模长 - const targetNodeNorm2 = targetItemVector.norm2(); - // 元素向量 - const itemVector = new Vector(item); - // 元素向量的模长 - const itemNorm2 = itemVector.norm2(); - // 计算元素向量和目标元素向量的点积 - const dot = targetItemVector.dot(itemVector); - const norm2Product = targetNodeNorm2 * itemNorm2; - // 计算元素向量和目标元素向量的余弦相似度 - const cosineSimilarity = norm2Product ? dot / norm2Product : 0; - return cosineSimilarity; -} - -export default cosineSimilarity; diff --git a/packages/graph/src/degree.ts b/packages/graph/src/degree.ts deleted file mode 100644 index 8a58adc..0000000 --- a/packages/graph/src/degree.ts +++ /dev/null @@ -1,51 +0,0 @@ -import { GraphData, DegreeType } from "./types"; - -const degree = (graphData: GraphData): DegreeType => { - const degrees: DegreeType = {}; - const { nodes = [], edges = [] } = graphData - - nodes.forEach((node) => { - degrees[node.id] = { - degree: 0, - inDegree: 0, - outDegree: 0, - }; - }); - - edges.forEach((edge) => { - degrees[edge.source].degree++; - degrees[edge.source].outDegree++; - degrees[edge.target].degree++; - degrees[edge.target].inDegree++; - }); - - return degrees; -}; - -export default degree; - -/** - * 获取指定节点的入度 - * @param graphData 图数据 - * @param nodeId 节点ID - */ -export const getInDegree = (graphData: GraphData, nodeId: string): number => { - const nodeDegree = degree(graphData) - if (nodeDegree[nodeId]) { - return degree(graphData)[nodeId].inDegree - } - return 0 -} - -/** - * 获取指定节点的出度 - * @param graphData 图数据 - * @param nodeId 节点ID - */ -export const getOutDegree = (graphData: GraphData, nodeId: string): number => { - const nodeDegree = degree(graphData) - if (nodeDegree[nodeId]) { - return degree(graphData)[nodeId].outDegree - } - return 0 -} diff --git a/packages/graph/src/detect-cycle.ts b/packages/graph/src/detect-cycle.ts deleted file mode 100644 index e6f1b25..0000000 --- a/packages/graph/src/detect-cycle.ts +++ /dev/null @@ -1,346 +0,0 @@ -import dfs from './dfs'; -import getConnectedComponents, { detectStrongConnectComponents } from './connected-component'; -import { GraphData, IAlgorithmCallbacks, NodeConfig } from './types'; -import { getNeighbors } from './util'; - -const detectDirectedCycle = (graphData: GraphData): { - [key: string]: string; -} => { - let cycle: { - [key: string]: string; - } = null; - - const { nodes = [] } = graphData - - const dfsParentMap = {}; - - // 所有没有被访问的节点集合 - const unvisitedSet = {}; - - // 正在被访问的节点集合 - const visitingSet = {}; - - // 所有已经被访问过的节点集合 - const visitedSet = {}; - - // 初始化 unvisitedSet - nodes.forEach((node) => { - unvisitedSet[node.id] = node; - }); - - const callbacks: IAlgorithmCallbacks = { - enter: ({ current: currentNode, previous: previousNode }) => { - if (visitingSet[currentNode]) { - // 如果当前节点正在访问中，则说明检测到环路了 - cycle = {}; - - let currentCycleNode = currentNode; - let previousCycleNode = previousNode; - - while (previousCycleNode !== currentNode) { - cycle[currentCycleNode] = previousCycleNode; - currentCycleNode = previousCycleNode; - previousCycleNode = dfsParentMap[previousCycleNode]; - } - - cycle[currentCycleNode] = previousCycleNode; - } else { - // 如果不存在正在访问集合中，则将其放入正在访问集合，并从未访问集合中删除 - visitingSet[currentNode] = currentNode; - delete unvisitedSet[currentNode]; - - // 更新 DSF parents 列表 - dfsParentMap[currentNode] = previousNode; - } - }, - leave: ({ current: currentNode }) => { - // 如果所有的节点的子节点都已经访问过了，则从正在访问集合中删除掉，并将其移入到已访问集合中， - // 同时也意味着当前节点的所有邻居节点都被访问过了 - visitedSet[currentNode] = currentNode; - delete visitingSet[currentNode]; - }, - allowTraversal: ({ next: nextNode }) => { - // 如果检测到环路则需要终止所有进一步的遍历，否则会导致无限循环遍历 - if (cycle) { - return false; - } - - // 仅允许遍历没有访问的节点，visitedSet 中的都已经访问过了 - return !visitedSet[nextNode]; - }, - }; - - // 开始遍历节点 - while (Object.keys(unvisitedSet).length) { - // 从第一个节点开始进行 DFS 遍历 - const firsetUnVisitedKey = Object.keys(unvisitedSet)[0]; - - dfs(graphData, firsetUnVisitedKey, callbacks); - } - - return cycle; -}; - -/** - * 检测无向图中的所有Base cycles - * refer: https://www.codeproject.com/Articles/1158232/Enumerating-All-Cycles-in-an-Undirected-Graph - * @param graph - * @param nodeIds 节点 ID 的数组 - * @param include 包含或排除指定的节点 - * @return [{[key: string]: INode}] 返回一组base cycles - */ -export const detectAllUndirectedCycle = (graphData: GraphData, nodeIds?: string[], include = true) => { - const allCycles = []; - const components = getConnectedComponents(graphData, false); - - // loop through all connected components - for (const component of components) { - if (!component.length) continue; - const root = component[0]; - const rootId = root.id; - - const stack = [root]; - const parent = { [rootId]: root }; - const used = { [rootId]: new Set() }; - - // walk a spanning tree to find cycles - while (stack.length > 0) { - const curNode = stack.pop(); - const curNodeId = curNode.id; - const neighbors = getNeighbors(curNodeId, graphData.edges); - for (let i = 0; i < neighbors.length; i += 1) { - const neighborId = neighbors[i]; - const neighbor = graphData.nodes.find(node => node.id === neighborId) - // const neighborId = neighbor.get('id'); - if (neighborId === curNodeId) { - // 自环 - allCycles.push({ [neighborId]: curNode }); - } else if (!(neighborId in used)) { - // visit a new node - parent[neighborId] = curNode; - stack.push(neighbor); - used[neighborId] = new Set([curNode]); - } else if (!used[curNodeId].has(neighbor)) { - // a cycle found - let cycleValid = true; - const cyclePath = [neighbor, curNode]; - let p = parent[curNodeId]; - while (used[neighborId].size && !used[neighborId].has(p)) { - cyclePath.push(p); - if (p === parent[p.id]) break; - else p = parent[p.id]; - } - cyclePath.push(p); - - if (nodeIds && include) { - // 如果有指定包含的节点 - cycleValid = false; - if (cyclePath.findIndex((node) => nodeIds.indexOf(node.id) > -1) > -1) { - cycleValid = true; - } - } else if (nodeIds && !include) { - // 如果有指定不包含的节点 - if (cyclePath.findIndex((node) => nodeIds.indexOf(node.id) > -1) > -1) { - cycleValid = false; - } - } - - // 把 node list 形式转换为 cycle 的格式 - if (cycleValid) { - const cycle = {}; - for (let index = 1; index < cyclePath.length; index += 1) { - cycle[cyclePath[index - 1].id] = cyclePath[index]; - } - if (cyclePath.length) { - cycle[cyclePath[cyclePath.length - 1].id] = cyclePath[0]; - } - allCycles.push(cycle); - } - - used[neighborId].add(curNode); - } - } - } - } - - return allCycles; -}; - -/** - * Johnson's algorithm, 时间复杂度 O((V + E)(C + 1))$ and space bounded by O(V + E) - * refer: https://www.cs.tufts.edu/comp/150GA/homeworks/hw1/Johnson%2075.PDF - * refer: https://networkx.github.io/documentation/stable/_modules/networkx/algorithms/cycles.html#simple_cycles - * @param graph - * @param nodeIds 节点 ID 的数组 - * @param include 包含或排除指定的节点 - * @return [{[key: string]: INode}] 返回所有的 simple cycles - */ -export const detectAllDirectedCycle = (graphData: GraphData, nodeIds?: string[], include = true) => { - const path = []; // stack of nodes in current path - const blocked = new Set(); - const B = []; // remember portions of the graph that yield no elementary circuit - const allCycles = []; - const idx2Node: { - [key: string]: NodeConfig; - } = {}; - const node2Idx = {}; - - // 辅助函数： unblock all blocked nodes - const unblock = (thisNode: NodeConfig) => { - const stack = [thisNode]; - while (stack.length > 0) { - const node = stack.pop(); - if (blocked.has(node)) { - blocked.delete(node); - B[node.id].forEach((n) => { - stack.push(n); - }); - B[node.id].clear(); - } - } - }; - - const circuit = (node: NodeConfig, start: NodeConfig, adjList) => { - let closed = false; // whether a path is closed - if (nodeIds && include === false && nodeIds.indexOf(node.id) > -1) return closed; - path.push(node); - blocked.add(node); - - const neighbors = adjList[node.id]; - for (let i = 0; i < neighbors.length; i += 1) { - const neighbor = idx2Node[neighbors[i]]; - if (neighbor === start) { - const cycle = {}; - for (let index = 1; index < path.length; index += 1) { - cycle[path[index - 1].id] = path[index]; - } - if (path.length) { - cycle[path[path.length - 1].id] = path[0]; - } - allCycles.push(cycle); - closed = true; - } else if (!blocked.has(neighbor)) { - if (circuit(neighbor, start, adjList)) { - closed = true; - } - } - } - - if (closed) { - unblock(node); - } else { - for (let i = 0; i < neighbors.length; i += 1) { - const neighbor = idx2Node[neighbors[i]]; - if (!B[neighbor.id].has(node)) { - B[neighbor.id].add(node); - } - } - } - path.pop(); - return closed; - }; - - const { nodes = [] } = graphData; - - // Johnson's algorithm 要求给节点赋顺序，先按节点在数组中的顺序 - for (let i = 0; i < nodes.length; i += 1) { - const node = nodes[i]; - const nodeId = node.id; - node2Idx[nodeId] = i; - idx2Node[i] = node; - } - // 如果有指定包含的节点，则把指定节点排序在前，以便提早结束搜索 - if (nodeIds && include) { - for (let i = 0; i < nodeIds.length; i++) { - const nodeId = nodeIds[i]; - node2Idx[nodes[i].id] = node2Idx[nodeId]; - node2Idx[nodeId] = 0; - idx2Node[0] = nodes.find(node => node.id === nodeId); - idx2Node[node2Idx[nodes[i].id]] = nodes[i]; - } - } - - // 返回 节点顺序 >= nodeOrder 的强连通分量的adjList - const getMinComponentAdj = (components: NodeConfig[][]) => { - let minCompIdx; - let minIdx = Infinity; - - // Find least component and the lowest node - for (let i = 0; i < components.length; i += 1) { - const comp = components[i]; - for (let j = 0; j < comp.length; j++) { - const nodeIdx = node2Idx[comp[j].id]; - if (nodeIdx < minIdx) { - minIdx = nodeIdx; - minCompIdx = i; - } - } - } - - const component = components[minCompIdx]; - const adjList = []; - for (let i = 0; i < component.length; i += 1) { - const node = component[i]; - adjList[node.id] = []; - for (const neighbor of getNeighbors(node.id, graphData.edges, 'target').filter((n) => component.map(c => c.id).indexOf(n) > -1)) { - // 对自环情况 (点连向自身) 特殊处理：记录自环，但不加入adjList - if (neighbor === node.id && !(include === false && nodeIds.indexOf(node.id) > -1)) { - allCycles.push({ [node.id]: node }); - } else { - adjList[node.id].push(node2Idx[neighbor]); - } - } - } - - return { - component, - adjList, - minIdx, - }; - }; - - let nodeIdx = 0; - while (nodeIdx < nodes.length) { - const subgraphNodes = nodes.filter((n) => node2Idx[n.id] >= nodeIdx); - const sccs = detectStrongConnectComponents({ nodes: subgraphNodes, edges: graphData.edges }).filter( - (component) => component.length > 1, - ); - if (sccs.length === 0) break; - - const scc = getMinComponentAdj(sccs); - const { minIdx, adjList, component } = scc; - if (component.length > 1) { - component.forEach((node) => { - B[node.id] = new Set(); - }); - const startNode = idx2Node[minIdx]; - // startNode 不在指定要包含的节点中，提前结束搜索 - if (nodeIds && include && nodeIds.indexOf(startNode.id) === -1) return allCycles; - circuit(startNode, startNode, adjList); - nodeIdx = minIdx + 1; - } else { - break; - } - } - return allCycles; -}; - -/** - * 查找图中所有满足要求的圈 - * @param graph - * @param directed 是否为有向图 - * @param nodeIds 节点 ID 的数组，若不指定，则返回图中所有的圈 - * @param include 包含或排除指定的节点 - * @return [{[key: string]: Node}] 包含所有环的数组，每个环用一个Object表示，其中key为节点id，value为该节点在环中指向的下一个节点 - */ -export const detectAllCycles = ( - graphData: GraphData, - directed?: boolean, - nodeIds?: string[], - include = true, -) => { - if (directed) return detectAllDirectedCycle(graphData, nodeIds, include); - return detectAllUndirectedCycle(graphData, nodeIds, include); -}; - -export default detectDirectedCycle; diff --git a/packages/graph/src/dfs.ts b/packages/graph/src/dfs.ts deleted file mode 100644 index cb0166c..0000000 --- a/packages/graph/src/dfs.ts +++ /dev/null @@ -1,76 +0,0 @@ -import { IAlgorithmCallbacks, GraphData } from './types' -import { getNeighbors } from './util' - -function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks) { - const initiatedCallback = callbacks; - - const stubCallback = () => {}; - - const allowTraversalCallback = (() => { - const seen = {}; - return ({ next }) => { - if (!seen[next]) { - seen[next] = true; - return true; - } - return false; - }; - })(); - - initiatedCallback.allowTraversal = callbacks.allowTraversal || allowTraversalCallback; - initiatedCallback.enter = callbacks.enter || stubCallback; - initiatedCallback.leave = callbacks.leave || stubCallback; - - return initiatedCallback; -} - -/** - * @param {Graph} graph - * @param {GraphNode} currentNode - * @param {GraphNode} previousNode - * @param {Callbacks} callbacks - */ -function depthFirstSearchRecursive( - graphData: GraphData, - currentNode: string, - previousNode: string, - callbacks: IAlgorithmCallbacks, -) { - callbacks.enter({ - current: currentNode, - previous: previousNode, - }); - - const { edges = [] } = graphData - - getNeighbors(currentNode, edges, 'target').forEach((nextNode) => { - if ( - callbacks.allowTraversal({ - previous: previousNode, - current: currentNode, - next: nextNode, - }) - ) { - depthFirstSearchRecursive(graphData, nextNode, currentNode, callbacks); - } - }); - - callbacks.leave({ - current: currentNode, - previous: previousNode, - }); -} - -/** - * 深度优先遍历图 - * @param data GraphData 图数据 - * @param startNodeId 开始遍历的节点的 ID - * @param originalCallbacks 回调 - */ -export default function depthFirstSearch( - graphData: GraphData, - startNodeId: string, - callbacks?: IAlgorithmCallbacks, -) { - depthFirstSearchRecursive(graphData, startNodeId, '', initCallbacks(callbacks)); -} diff --git a/packages/graph/src/dijkstra.ts b/packages/graph/src/dijkstra.ts index a557215..5ffa2d9 100644 --- a/packages/graph/src/dijkstra.ts +++ b/packages/graph/src/dijkstra.ts @@ -1,13 +1,12 @@ +import { Edge, ID, Node } from "@antv/graphlib"; import { isArray } from '@antv/util'; -import { GraphData, NodeConfig, EdgeConfig } from './types'; -import { getOutEdgesNodeId, getEdgesByNodeId } from './util'; +import { EdgeData, Graph, NodeData } from "./types"; -const minVertex = ( - D: { [key: string]: number }, - nodes: NodeConfig[], +function minVertex( + D: { [key: ID]: number }, + nodes: Node[], marks: { [key: string]: boolean }, -): NodeConfig => { - // 找出最小的点 +): Node { let minDis = Infinity; let minNode; for (let i = 0; i < nodes.length; i++) { @@ -18,19 +17,41 @@ const minVertex = ( } } return minNode; -}; +} + +function findAllPaths(source: ID, target: ID, prevs: Record, foundPaths: Record): ID[] { + if (source === target) { + return [source]; + } + if (foundPaths[target]) { + // @ts-ignore + return foundPaths[target]; + } + const paths: ID[][] = []; + for (const prev of prevs[target]) { + const prevPaths = findAllPaths(source, prev, prevs, foundPaths); + if (!prevPaths) return; + for (const prePath of prevPaths) { + if (isArray(prePath)) paths.push([...prePath, target]); + else paths.push([prePath, target]); + } + } + foundPaths[target] = paths; + // @ts-ignore + return foundPaths[target]; +} -const dijkstra = ( - graphData: GraphData, - source: string, +export function dijkstra( + graph: Graph, + source: ID, directed?: boolean, weightPropertyName?: string, -) => { - const { nodes = [], edges = [] } = graphData; - const nodeIds = []; - const marks = {}; - const D = {}; - const prevs = {}; // key: 顶点, value: 顶点的前驱点数组（可能有多条等长的最短路径） +) { + const nodes = graph.getAllNodes(); + const nodeIds: ID[] = []; + const marks: Record = {}; + const D: Record = {}; + const prevs: Record = {}; // key: 顶点, value: 顶点的前驱点数组（可能有多条等长的最短路径） nodes.forEach((node, i) => { const id = node.id; nodeIds.push(id); @@ -47,15 +68,18 @@ const dijkstra = ( if (D[minNodeId] === Infinity) continue; // Unreachable vertices cannot be the intermediate point - let relatedEdges: EdgeConfig[] = []; - if (directed) relatedEdges = getOutEdgesNodeId(minNodeId, edges); - else relatedEdges = getEdgesByNodeId(minNodeId, edges); + let relatedEdges: Edge[] = []; + if (directed) { + relatedEdges = graph.getRelatedEdges(minNodeId, 'out'); + } else { + relatedEdges = graph.getRelatedEdges(minNodeId, 'both'); + } - relatedEdges.forEach(edge => { + relatedEdges.forEach((edge) => { const edgeTarget = edge.target; const edgeSource = edge.source; const w = edgeTarget === minNodeId ? edgeSource : edgeTarget; - const weight = weightPropertyName && edge[weightPropertyName] ? edge[weightPropertyName] : 1; + const weight = weightPropertyName && edge.data[weightPropertyName] as number ? edge.data[weightPropertyName] as number : 1; if (D[w] > D[minNode.id] + weight) { D[w] = D[minNode.id] + weight; prevs[w] = [minNode.id]; @@ -67,7 +91,7 @@ const dijkstra = ( prevs[source] = [source]; // 每个节点存可能存在多条最短路径 - const paths = {}; + const paths: Record = {}; for (const target in D) { if (D[target] !== Infinity) { findAllPaths(source, target, prevs, paths); @@ -75,31 +99,9 @@ const dijkstra = ( } // 兼容之前单路径 - const path = {}; + const path: Record = {}; for (const target in paths) { path[target] = paths[target][0]; } return { length: D, path, allPath: paths }; -}; - -export default dijkstra; - -function findAllPaths(source, target, prevs, foundPaths) { - if (source === target) { - return [source]; - } - if (foundPaths[target]) { - return foundPaths[target]; - } - const paths = []; - for (let prev of prevs[target]) { - const prevPaths = findAllPaths(source, prev, prevs, foundPaths); - if (!prevPaths) return; - for (let prePath of prevPaths) { - if (isArray(prePath)) paths.push([...prePath, target]); - else paths.push([prePath, target]); - } - } - foundPaths[target] = paths; - return foundPaths[target]; -} +} \ No newline at end of file diff --git a/packages/graph/src/find-path.ts b/packages/graph/src/findPath.ts similarity index 59% rename from packages/graph/src/find-path.ts rename to packages/graph/src/findPath.ts index 846d46e..b0a5e55 100644 --- a/packages/graph/src/find-path.ts +++ b/packages/graph/src/findPath.ts @@ -1,16 +1,16 @@ -import dijkstra from './dijkstra'; -import { GraphData } from './types'; -import { getNeighbors } from './util'; +import { ID, Node } from "@antv/graphlib"; +import { Graph, NodeData } from "./types"; +import { dijkstra } from "./dijkstra"; export const findShortestPath = ( - graphData: GraphData, - start: string, - end: string, + graph: Graph, + start: ID, + end: ID, directed?: boolean, weightPropertyName?: string ) => { const { length, path, allPath } = dijkstra( - graphData, + graph, start, directed, weightPropertyName @@ -19,22 +19,20 @@ export const findShortestPath = ( }; export const findAllPath = ( - graphData: GraphData, - start: string, - end: string, + graph: Graph, + start: ID, + end: ID, directed?: boolean ) => { if (start === end) return [[start]]; - const { edges = [] } = graphData; - const visited = [start]; const isVisited = { [start]: true }; - const stack: string[][] = []; // 辅助栈，用于存储访问过的节点的邻居节点 + const stack: Node[][] = []; // 辅助栈，用于存储访问过的节点的邻居节点 const allPath = []; let neighbors = directed - ? getNeighbors(start, edges, 'target') - : getNeighbors(start, edges); + ? graph.getSuccessors(start) + : graph.getNeighbors(start); stack.push(neighbors); while (visited.length > 0 && stack.length > 0) { @@ -42,12 +40,12 @@ export const findAllPath = ( if (children.length) { const child = children.shift(); if (child) { - visited.push(child); - isVisited[child] = true; + visited.push(child.id); + isVisited[child.id] = true; neighbors = directed - ? getNeighbors(child, edges, 'target') - : getNeighbors(child, edges); - stack.push(neighbors.filter(neighbor => !isVisited[neighbor])); + ? graph.getSuccessors(child.id) + : graph.getNeighbors(child.id); + stack.push(neighbors.filter((neighbor) => !isVisited[neighbor.id])); } } else { const node = visited.pop(); @@ -57,7 +55,7 @@ export const findAllPath = ( } if (visited[visited.length - 1] === end) { - const path = visited.map(node => node); + const path = visited.map((node) => node); allPath.push(path); const node = visited.pop(); @@ -67,4 +65,4 @@ export const findAllPath = ( } return allPath; -}; +}; \ No newline at end of file diff --git a/packages/graph/src/floydWarshall.ts b/packages/graph/src/floydWarshall.ts deleted file mode 100644 index 70e2ffe..0000000 --- a/packages/graph/src/floydWarshall.ts +++ /dev/null @@ -1,34 +0,0 @@ -import getAdjMatrix from "./adjacent-matrix"; -import { GraphData, Matrix } from "./types"; - -const floydWarshall = (graphData: GraphData, directed?: boolean) => { - const adjacentMatrix = getAdjMatrix(graphData, directed); - - const dist: Matrix[] = []; - const size = adjacentMatrix.length; - for (let i = 0; i < size; i += 1) { - dist[i] = []; - for (let j = 0; j < size; j += 1) { - if (i === j) { - dist[i][j] = 0; - } else if (adjacentMatrix[i][j] === 0 || !adjacentMatrix[i][j]) { - dist[i][j] = Infinity; - } else { - dist[i][j] = adjacentMatrix[i][j]; - } - } - } - // floyd - for (let k = 0; k < size; k += 1) { - for (let i = 0; i < size; i += 1) { - for (let j = 0; j < size; j += 1) { - if (dist[i][j] > dist[i][k] + dist[k][j]) { - dist[i][j] = dist[i][k] + dist[k][j]; - } - } - } - } - return dist; -}; - -export default floydWarshall; diff --git a/packages/graph/src/gSpan/gSpan.ts b/packages/graph/src/gSpan/gSpan.ts deleted file mode 100644 index 6e68cd8..0000000 --- a/packages/graph/src/gSpan/gSpan.ts +++ /dev/null @@ -1,954 +0,0 @@ -import { GraphData } from "../types"; -import { clone } from "@antv/util"; -import { - Graph, - Edge, - VACANT_NODE_LABEL, - VACANT_GRAPH_ID, - Node, - VACANT_EDGE_LABEL, -} from "./struct"; - -export interface EdgeMap { - [key: string]: { - // key 的格式为 source-target - idx: number; // 该边在原图 graphData.edges 的序号 - edge: any; - }; -} - -export interface NodeMap { - [key: string]: { - // key 格式为 node.id - idx: number; // 该j客店在原图 graphData.nodes 的序号 - node: any; - degree: number; - inDegree: number; - outDegree: number; - }; -} - -interface PDFS { - graphId: number; - edge: any; - preNode: any; -} - -class DFSedge { - public fromNode: number; - public toNode: number; - public nodeEdgeNodeLabel: { - nodeLabel1: string; - edgeLabel: string; - nodeLabel2: string; - }; - - constructor( - fromNode: number, - toNode: number, - fromNodeLabel: string, - edgeLabel: string, - toNodeLabel: string - ) { - this.fromNode = fromNode; - this.toNode = toNode; - this.nodeEdgeNodeLabel = { - nodeLabel1: fromNodeLabel || VACANT_NODE_LABEL, - edgeLabel: edgeLabel || VACANT_EDGE_LABEL, - nodeLabel2: toNodeLabel || VACANT_NODE_LABEL, - }; - } - - equalTo(other) { - return ( - this.fromNode === other.formNode && - this.toNode === other.toNode && - this.nodeEdgeNodeLabel === other.nodeEdgeNodeLabel - ); - } - - notEqualTo(other) { - return !this.equalTo(other); - } -} - -// DFScode 是 DESedge 的数组 -class DFScode { - public dfsEdgeList: DFSedge[]; - public rmpath: any; - - constructor() { - this.rmpath = []; - this.dfsEdgeList = []; - } - - equalTo(other) { - const aLength = this.dfsEdgeList.length; - const bLength = other.length; - if (aLength !== bLength) return false; - for (let i = 0; i < aLength; i++) { - if (this.dfsEdgeList[i] !== other[i]) return false; - } - return true; - } - - notEqualTo(other) { - return !this.equalTo(other); - } - - /** 增加一条 edge 到 DFScode */ - pushBack(fromNode, toNode, fromNodeLabel, edgeLabel, toNodeLabel) { - this.dfsEdgeList.push( - new DFSedge(fromNode, toNode, fromNodeLabel, edgeLabel, toNodeLabel) - ); - return this.dfsEdgeList; - } - - /** 根据 dfs 构建图 */ - toGraph(graphId: number = VACANT_GRAPH_ID, directed = false) { - const graph = new Graph(graphId, true, directed); - this.dfsEdgeList.forEach((dfsEdge) => { - const fromNodeId = dfsEdge.fromNode; - const toNodeId = dfsEdge.toNode; - const { nodeLabel1, edgeLabel, nodeLabel2 } = dfsEdge.nodeEdgeNodeLabel; - - if (nodeLabel1 !== VACANT_NODE_LABEL) graph.addNode(fromNodeId, nodeLabel1); - if (nodeLabel2 !== VACANT_NODE_LABEL) graph.addNode(toNodeId, nodeLabel2); - if (nodeLabel1 !== VACANT_NODE_LABEL && nodeLabel2 !== nodeLabel1) graph.addEdge(undefined, fromNodeId, toNodeId, edgeLabel); - }); - return graph; - } - - // 建立 rightmost path - buildRmpath() { - this.rmpath = []; - let oldFrom = undefined; - const selfLength = this.dfsEdgeList.length; - for (let i = selfLength - 1; i >= 0; i--) { - const dfsEdge = this.dfsEdgeList[i]; - const fromNodeIdx = dfsEdge.fromNode; - const toNodeIdx = dfsEdge.toNode; - if ( - fromNodeIdx < toNodeIdx && - (oldFrom === undefined || toNodeIdx === oldFrom) - ) { - this.rmpath.push(i); - oldFrom = fromNodeIdx; - } - } - return this.rmpath; - } - - getNodeNum() { - const nodeMap = {}; - this.dfsEdgeList.forEach((dfsEdge) => { - if (!nodeMap[dfsEdge.fromNode]) nodeMap[dfsEdge.fromNode] = true; - if (!nodeMap[dfsEdge.toNode]) nodeMap[dfsEdge.toNode] = true; - }); - return Object.keys(nodeMap).length; - } -} - -class History { - public his: object; - public edges: Edge[]; - public nodesUsed: object; - public edgesUsed: object; - - constructor(pdfs: PDFS) { - this.his = {}; - this.nodesUsed = {}; - this.edgesUsed = {}; - this.edges = []; - if (!pdfs) return; - while (pdfs) { - const e = pdfs.edge; - this.edges.push(e); - this.nodesUsed[e.from] = 1; - this.nodesUsed[e.to] = 1; - this.edgesUsed[e.id] = 1; - pdfs = pdfs.preNode; - } - // 倒序 - this.edges = this.edges.reverse(); - } - - hasNode(node: Node) { - return this.nodesUsed[node.id] === 1; - } - - hasEdge(edge: Edge) { - return this.edgesUsed[edge.id] === 1; - } -} - -interface Root { - [key: string]: { - projected: PDFS[]; - nodeLabel1?: string; - edgeLabel?: string; - nodeLabel2?: string; - fromNodeId?: number; - toNodeId?: number; - }; -} - -interface GraphDataMap { - [key: string]: GraphData; -} -interface GraphMap { - [key: number]: Graph; -} - -interface AlgorithmProps { - graphs: GraphMap; // 图数据 - minSupport: number; // 算法参数，最小支持数量，根据 graphs 内图的数量指定 - directed?: boolean; // 是否有向图，默认为 false - minNodeNum?: number; // 每个子图中边的最少个数，默认为 1 - maxNodeNum?: number; // 每个子图中边的最多个数，默认为 4 - top?: number; // 返回前 top 个频繁子图，默认为 10 - verbose?: boolean; -} - -class GSpan { - public graphs: GraphMap; - public dfsCode: DFScode; - public support: number; - public frequentSize1Subgraphs: GraphData[]; - public frequentSubgraphs: Graph[]; - public reportDF: []; - public maxNodeNum: number; - public minNodeNum: number; - public minSupport: number; - public top: number; - public directed: boolean; - private counter: number; // 用于生成图的 id，自增 - public verbose: boolean; - - constructor({ - graphs, - minSupport = 2, - minNodeNum = 1, - maxNodeNum = 4, - top = 10, - directed = false, - verbose = false, - }: AlgorithmProps) { - // -------- 第零步，初始化------- - this.graphs = graphs; - this.dfsCode = new DFScode(); - this.support = 0; - this.frequentSize1Subgraphs = []; - this.frequentSubgraphs = []; - this.minSupport = minSupport; - this.top = top; - this.directed = directed; - this.counter = 0; - // TODO? timestamp = {} - this.maxNodeNum = maxNodeNum; - this.minNodeNum = minNodeNum; - this.verbose = verbose; - if (this.maxNodeNum < this.minNodeNum) this.maxNodeNum = this.minNodeNum; - this.reportDF = []; // matrix - } - - // Line 352 - findForwardRootEdges(graph: Graph, fromNode: Node): Edge[] { - const result = []; - const nodeMap = graph.nodeMap; - fromNode.edges.forEach((edge) => { - if (this.directed || fromNode.label <= nodeMap[edge.to].label) - result.push(edge); - }); - - return result; - } - - findBackwardEdge( - graph: Graph, - edge1: Edge, - edge2: Edge, - history: History - ): Edge { - if (!this.directed && edge1 === edge2) return null; - const nodeMap = graph.nodeMap; - const edge2To = nodeMap[edge2.to]; - const edge2ToEdges = edge2To.edges; - const edgeLength = edge2ToEdges.length; - for (let i = 0; i < edgeLength; i++) { - const edge = edge2ToEdges[i]; - if (history.hasEdge(edge) || edge.to !== edge1.from) continue; - if (!this.directed) { - if ( - edge1.label < edge.label || - (edge1.label === edge.label && - nodeMap[edge1.to].label <= nodeMap[edge2.to].label) - ) { - return edge; - } - } else { - if ( - nodeMap[edge1.from].label < nodeMap[edge2.to].label || - (nodeMap[edge1.from].label === nodeMap[edge2.to].label && - edge1.label <= edge.label) - ) { - return edge; - } - } - } - return null; - } - - findForwardPureEdges( - graph, - rightmostEdge, - minNodeLabel, - history: History - ): Edge[] { - const result = []; - const rightmostEdgeToId = rightmostEdge.to; - const edges = graph.nodeMap[rightmostEdgeToId].edges; - const edgeLength = edges.length; - for (let i = 0; i < edgeLength; i++) { - const edge = edges[i]; - const toNode = graph.nodeMap[edge.to]; - if (minNodeLabel <= toNode.label && !history.hasNode(toNode)) { - result.push(edge); - } - } - return result; - } - - findForwardRmpathEdges( - graph: Graph, - rightmostEdge: Edge, - minNodeLabel: string, - history: History - ): Edge[] { - const result = []; - const nodeMap = graph.nodeMap; - const toNodeLabel = nodeMap[rightmostEdge.to].label; - const fromNode = nodeMap[rightmostEdge.from]; - const edges = fromNode.edges; - const edgeLength = edges.length; - for (let i = 0; i < edgeLength; i++) { - const edge = edges[i]; - const newToNodeLabel = nodeMap[edge.to].label; - if ( - rightmostEdge.to === edge.to || - minNodeLabel > newToNodeLabel || - history.hasNode(nodeMap[edge.to]) - ) { - continue; - } - if ( - rightmostEdge.label < edge.label || - (rightmostEdge.label === edge.label && toNodeLabel <= newToNodeLabel) - ) { - result.push(edge); - } - } - return result; - } - - getSupport(projected: PDFS[]): number { - const graphMap = {}; - projected.forEach((pro) => { - if (!graphMap[pro.graphId]) graphMap[pro.graphId] = true; - }); - return Object.keys(graphMap).length; - } - - findMinLabel( - obj: Root - ): { - nodeLabel1?: string; - edgeLabel: string; - nodeLabel2?: string; - } { - let minLabel = undefined; - Object.keys(obj).forEach((nodeEdgeNodeLabel) => { - const { nodeLabel1, edgeLabel, nodeLabel2 } = obj[nodeEdgeNodeLabel]; - if (!minLabel) { - minLabel = { - nodeLabel1, - edgeLabel, - nodeLabel2, - }; - return; - } - if ( - nodeLabel1 < minLabel.nodeLabel1 || - (nodeLabel1 === minLabel.nodeLabel1 && - edgeLabel < minLabel.edgeLabel) || - (nodeLabel1 === minLabel.nodeLabel1 && - edgeLabel === minLabel.edgeLabel && - nodeLabel2 < minLabel.nodeLabel2) - ) { - minLabel = { - nodeLabel1, - edgeLabel, - nodeLabel2, - }; - } - }); - return minLabel; - } - - isMin() { - const dfsCode = this.dfsCode; - if (this.verbose) console.log("isMin checking", dfsCode); - if (dfsCode.dfsEdgeList.length === 1) return true; - const directed = this.directed; - const graph = dfsCode.toGraph(VACANT_GRAPH_ID, directed); - const nodeMap = graph.nodeMap; - const dfsCodeMin = new DFScode(); - const root: Root = {}; - graph.nodes.forEach((node) => { - const forwardEdges = this.findForwardRootEdges(graph, node); - forwardEdges.forEach((edge) => { - let otherNode = nodeMap[edge.to]; - const nodeEdgeNodeLabel = `${node.label}-${edge.label}-${otherNode.label}`; - if (!root[nodeEdgeNodeLabel]) - root[nodeEdgeNodeLabel] = { - projected: [], - nodeLabel1: node.label, - edgeLabel: edge.label, - nodeLabel2: otherNode.label, - }; - const pdfs: PDFS = { - graphId: graph.id, - edge, - preNode: null, - }; - root[nodeEdgeNodeLabel].projected.push(pdfs); - }); - }); - - // 比较 root 中每一项的 nodeEdgeNodeLabel 大小，按照 nodeLabel1、edgeLabe、nodeLabel2 的顺序比较 - let minLabel = this.findMinLabel(root); // line 419 - if (!minLabel) return; - dfsCodeMin.dfsEdgeList.push( - new DFSedge( - 0, - 1, - minLabel.nodeLabel1, - minLabel.edgeLabel, - minLabel.nodeLabel2 - ) - ); - - // line 423 - const projectIsMin = (projected: PDFS[]) => { - // right most path - const rmpath = dfsCodeMin.buildRmpath(); - const minNodeLabel = - dfsCodeMin.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1; - const maxToC = dfsCodeMin.dfsEdgeList[rmpath[0]].toNode; // node id - - const backwardRoot: Root = {}; - let flag = false, - newTo = 0; - let end = directed ? -1 : 0; // 遍历到 1 还是到 0 - for (let i = rmpath.length - 1; i > end; i--) { - if (flag) break; - // line 435 - projected.forEach((p) => { - const history = new History(p); - const backwardEdge = this.findBackwardEdge( - graph, - history.edges[rmpath[i]], - history.edges[rmpath[0]], - history - ); - if (backwardEdge) { - // Line 441 - if (!backwardRoot[backwardEdge.label]) { - backwardRoot[backwardEdge.label] = { - projected: [], - edgeLabel: backwardEdge.label, - }; - } - backwardRoot[backwardEdge.label].projected.push({ - graphId: graph.id, - edge: backwardRoot, - preNode: p, - }); - newTo = dfsCodeMin.dfsEdgeList[rmpath[i]].fromNode; - flag = true; - } - }); - } - - if (flag) { - const minBackwardEdgeLabel = this.findMinLabel(backwardRoot); - dfsCodeMin.dfsEdgeList.push( - new DFSedge( - maxToC, - newTo, - VACANT_NODE_LABEL, - minBackwardEdgeLabel.edgeLabel, - VACANT_NODE_LABEL - ) - ); - const idx = dfsCodeMin.dfsEdgeList.length - 1; - if (this.dfsCode.dfsEdgeList[idx] !== dfsCodeMin.dfsEdgeList[idx]) - return false; - return projectIsMin( - backwardRoot[minBackwardEdgeLabel.edgeLabel].projected - ); - } - const forwardRoot: Root = {}; - flag = false; - let newFrom = 0; - projected.forEach((p) => { - const history = new History(p); - const forwardPureEdges = this.findForwardPureEdges( - graph, - history.edges[rmpath[0]], - minNodeLabel, - history - ); - if (forwardPureEdges.length > 0) { - flag = true; - newFrom = maxToC; - forwardPureEdges.forEach((edge) => { - const key = `${edge.label}-${nodeMap[edge.to].label}`; - if (!forwardRoot[key]) - forwardRoot[key] = { - projected: [], - edgeLabel: edge.label, - nodeLabel2: nodeMap[edge.to].label, - }; - forwardRoot[key].projected.push({ - graphId: graph.id, - edge, - preNode: p, - }); - }); - } - }); - - const pathLength = rmpath.length; - for (let i = 0; i < pathLength; i++) { - if (flag) break; - const value = rmpath[i]; - projected.forEach((p) => { - const history = new History(p); - const forwardRmpathEdges = this.findForwardRmpathEdges( - graph, - history.edges[value], - minNodeLabel, - history - ); - if (forwardRmpathEdges.length > 0) { - flag = true; - newFrom = dfsCodeMin.dfsEdgeList[value].fromNode; - forwardRmpathEdges.forEach((edge) => { - const key = `${edge.label}-${nodeMap[edge.to].label}`; - if (!forwardRoot[key]) - forwardRoot[key] = { - projected: [], - edgeLabel: edge.label, - nodeLabel2: nodeMap[edge.to].label, - }; - forwardRoot[key].projected.push({ - graphId: graph.id, - edge, - preNode: p, - }); - }); - } - }); - } - - if (!flag) return true; - - const forwardMinEdgeNodeLabel = this.findMinLabel(forwardRoot); - dfsCodeMin.dfsEdgeList.push( - new DFSedge( - newFrom, - maxToC + 1, - VACANT_NODE_LABEL, - forwardMinEdgeNodeLabel.edgeLabel, - forwardMinEdgeNodeLabel.nodeLabel2 - ) - ); - const idx = dfsCodeMin.dfsEdgeList.length - 1; - if (dfsCode.dfsEdgeList[idx] !== dfsCodeMin.dfsEdgeList[idx]) - return false; - return projectIsMin( - forwardRoot[ - `${forwardMinEdgeNodeLabel.edgeLabel}-${forwardMinEdgeNodeLabel.nodeLabel2}` - ].projected - ); - }; - const key = `${minLabel.nodeLabel1}-${minLabel.edgeLabel}-${minLabel.nodeLabel2}`; - return projectIsMin(root[key].projected); - } - - report() { - if (this.dfsCode.getNodeNum() < this.minNodeNum) return; - this.counter++; - const graph = this.dfsCode.toGraph(this.counter, this.directed); - this.frequentSubgraphs.push(clone(graph)); - } - - subGraphMining(projected) { - const support = this.getSupport(projected); - if (support < this.minSupport) return; - if (!this.isMin()) return; - this.report(); - - const nodeNum = this.dfsCode.getNodeNum(); - const rmpath = this.dfsCode.buildRmpath(); - const maxToC = this.dfsCode.dfsEdgeList[rmpath[0]].toNode; - const minNodeLabel = this.dfsCode.dfsEdgeList[0].nodeEdgeNodeLabel - .nodeLabel1; - - const forwardRoot: Root = {}; - const backwardRoot: Root = {}; - - projected.forEach((p) => { - const graph = this.graphs[p.graphId]; - const nodeMap = graph.nodeMap; - const history = new History(p); - // backward Line 526 - for (let i = rmpath.length - 1; i >= 0; i--) { - const backwardEdge = this.findBackwardEdge( - graph, - history.edges[rmpath[i]], - history.edges[rmpath[0]], - history - ); - if (backwardEdge) { - const key = `${this.dfsCode.dfsEdgeList[rmpath[i]].fromNode}-${ - backwardEdge.label - }`; - if (!backwardRoot[key]) - backwardRoot[key] = { - projected: [], - toNodeId: this.dfsCode.dfsEdgeList[rmpath[i]].fromNode, - edgeLabel: backwardEdge.label, - }; - backwardRoot[key].projected.push({ - graphId: p.graphId, - edge: backwardEdge, - preNode: p, - }); - } - } - - // pure forward - if (nodeNum >= this.maxNodeNum) return; - const forwardPureEdges = this.findForwardPureEdges( - graph, - history.edges[rmpath[0]], - minNodeLabel, - history - ); - forwardPureEdges.forEach((edge) => { - const key = `${maxToC}-${edge.label}-${nodeMap[edge.to].label}`; - if (!forwardRoot[key]) - forwardRoot[key] = { - projected: [], - fromNodeId: maxToC, - edgeLabel: edge.label, - nodeLabel2: nodeMap[edge.to].label, - }; - forwardRoot[key].projected.push({ - graphId: p.graphId, - edge, - preNode: p, - }); - }); - - // rmpath forward - for (let i = 0; i < rmpath.length; i++) { - const forwardRmpathEdges = this.findForwardRmpathEdges( - graph, - history.edges[rmpath[i]], - minNodeLabel, - history - ); - forwardRmpathEdges.forEach((edge) => { - const key = `${this.dfsCode.dfsEdgeList[rmpath[i]].fromNode}-${ - edge.label - }-${nodeMap[edge.to].label}`; - if (!forwardRoot[key]) - forwardRoot[key] = { - projected: [], - fromNodeId: this.dfsCode.dfsEdgeList[rmpath[i]].fromNode, - edgeLabel: edge.label, - nodeLabel2: nodeMap[edge.to].label, - }; - forwardRoot[key].projected.push({ - graphId: p.graphId, - edge, - preNode: p, - }); - }); - } - }); - - // backward - Object.keys(backwardRoot).forEach((key) => { - const { toNodeId, edgeLabel } = backwardRoot[key]; - this.dfsCode.dfsEdgeList.push( - new DFSedge(maxToC, toNodeId, "-1", edgeLabel, "-1") - ); - this.subGraphMining(backwardRoot[key].projected); - this.dfsCode.dfsEdgeList.pop(); - }); - - // forward - Object.keys(forwardRoot).forEach((key) => { - const { fromNodeId, edgeLabel, nodeLabel2 } = forwardRoot[key]; - this.dfsCode.dfsEdgeList.push( - new DFSedge( - fromNodeId, - maxToC + 1, - VACANT_NODE_LABEL, - edgeLabel, - nodeLabel2 - ) - ); - this.subGraphMining(forwardRoot[key].projected); - this.dfsCode.dfsEdgeList.pop(); - }); - } - - generate1EdgeFrequentSubGraphs() { - const graphs = this.graphs; - const directed = this.directed; - const minSupport = this.minSupport; - const frequentSize1Subgraphs = this.frequentSize1Subgraphs; - let nodeLabelCounter = {}, - nodeEdgeNodeCounter = {}; - // 保存各个图和各自节点的关系 map，key 格式为 graphKey-node类型 - const nodeLableCounted = {}; - // 保存各个图和各自边的关系 map，key 格式为 graphKey-fromNode类型-edge类型-toNode类型 - const nodeEdgeNodeLabelCounted = {}; - Object.keys(graphs).forEach((key) => { - // Line 271 - const graph = graphs[key]; - const nodeMap = graph.nodeMap; - // 遍历节点，记录对应图 与 每个节点的 label 到 nodeLableCounted - graph.nodes.forEach((node, i) => { - // Line 272 - const nodeLabel = node.label; - const graphNodeKey = `${key}-${nodeLabel}`; - if (!nodeLableCounted[graphNodeKey]) { - let counter = nodeLabelCounter[nodeLabel] || 0; - counter++; - nodeLabelCounter[nodeLabel] = counter; - } - nodeLableCounted[graphNodeKey] = { - graphKey: key, - label: nodeLabel, - }; - // 遍历该节点的所有边，记录各个图和各自边的关系到 nodeEdgeNodeLabelCounted. Line 276 - node.edges.forEach((edge) => { - let nodeLabel1 = nodeLabel; - let nodeLabel2 = nodeMap[edge.to].label; - if (!directed && nodeLabel1 > nodeLabel2) { - const tmp = nodeLabel2; - nodeLabel2 = nodeLabel1; - nodeLabel1 = tmp; - } - const edgeLabel = edge.label; - - const graphNodeEdgeNodeKey = `${key}-${nodeLabel1}-${edgeLabel}-${nodeLabel2}`; - const nodeEdgeNodeKey = `${nodeLabel1}-${edgeLabel}-${nodeLabel2}`; - - if (!nodeEdgeNodeCounter[nodeEdgeNodeKey]) { - let counter = nodeEdgeNodeCounter[nodeEdgeNodeKey] || 0; - counter++; - nodeEdgeNodeCounter[nodeEdgeNodeKey] = counter; // Line281 - } - nodeEdgeNodeLabelCounted[graphNodeEdgeNodeKey] = { - graphId: key, - nodeLabel1, - edgeLabel, - nodeLabel2, - }; - }); - }); - }); - - // 计算频繁的节点 - Object.keys(nodeLabelCounter).forEach((label) => { - const count = nodeLabelCounter[label]; - if (count < minSupport) return; - const g = { nodes: [], edges: [] }; - g.nodes.push({ - id: "0", - label, - }); - frequentSize1Subgraphs.push(g); - // if (minNodeNum <= 1) reportSize1 TODO - }); - - return frequentSize1Subgraphs; - } - - run() { - // -------- 第一步, _generate_1edge_frequent_subgraphs：频繁的单个节点------- - this.frequentSize1Subgraphs = this.generate1EdgeFrequentSubGraphs(); - - if (this.maxNodeNum < 2) return; - - const graphs = this.graphs; - const directed = this.directed; - - // PDFS 数组的 map Line 304 - const root: Root = {}; - Object.keys(graphs).forEach((graphId: any) => { - const graph = graphs[graphId]; - const nodeMap = graph.nodeMap; - // Line 306 - graph.nodes.forEach((node) => { - const forwardRootEdges = this.findForwardRootEdges(graph, node); - // Line 308 - forwardRootEdges.forEach((edge) => { - let toNode = nodeMap[edge.to]; - const nodeEdgeNodeLabel = `${node.label}-${edge.label}-${toNode.label}`; - if (!root[nodeEdgeNodeLabel]) - root[nodeEdgeNodeLabel] = { - projected: [], - nodeLabel1: node.label as string, - edgeLabel: edge.label as string, - nodeLabel2: toNode.label as string, - }; - const pdfs: PDFS = { - graphId, - edge, - preNode: null, - }; - root[nodeEdgeNodeLabel].projected.push(pdfs); - }); - }); - }); - - // Line 313 - Object.keys(root).forEach((nodeEdgeNodeLabel) => { - const { projected, nodeLabel1, edgeLabel, nodeLabel2 } = root[ - nodeEdgeNodeLabel - ]; - - this.dfsCode.dfsEdgeList.push( - new DFSedge(0, 1, nodeLabel1, edgeLabel, nodeLabel2) - ); - this.subGraphMining(projected); - this.dfsCode.dfsEdgeList.pop(); - }); - } -} - -const formatGraphs = ( - graphs: GraphDataMap, - directed: boolean, - nodeLabelProp: string, - edgeLabelProp: string -): GraphMap => { - const result: { [key: number]: Graph } = {}; - Object.keys(graphs).forEach((key, i) => { - const graph = graphs[key]; - const fGraph = new Graph(i, true, directed); - const nodeIdxMap = {}; - graph.nodes.forEach((node, j) => { - fGraph.addNode(j, node[nodeLabelProp]); - nodeIdxMap[node.id] = j; - }); - graph.edges.forEach((edge, k) => { - const sourceIdx = nodeIdxMap[edge.source]; - const targetIdx = nodeIdxMap[edge.target]; - fGraph.addEdge(-1, sourceIdx, targetIdx, edge[edgeLabelProp]); - }); - if (fGraph && fGraph.getNodeNum()) result[fGraph.id] = fGraph; - }); - return result; -}; - -const toGraphDatas = ( - graphs: Graph[], - nodeLabelProp: string, - edgeLabelProp: string -) => { - const result = []; - graphs.forEach((graph) => { - const graphData = { nodes: [], edges: [] }; - graph.nodes.forEach((node) => { - graphData.nodes.push({ - id: `${node.id}`, - [nodeLabelProp]: node.label, - }); - }); - graph.edges.forEach((edge) => { - graphData.edges.push({ - source: `${edge.from}`, - target: `${edge.to}`, - [edgeLabelProp]: edge.label, - }); - }); - result.push(graphData); - }); - return result; -}; - -interface Props { - graphs: GraphDataMap; // 图数据 - minSupport: number; // 算法参数，最小支持数量，根据 graphs 内图的数量指定 - directed?: boolean; // 是否有向图，默认为 false - nodeLabelProp?: string; // 节点类型的属性名 - edgeLabelProp?: string; // 边类型的属性名 - minNodeNum?: number; // 每个子图中节点的最少个数，默认为 1 - maxNodeNum?: number; // 每个子图中节点的最多个数，默认为 4 - top?: number; // 返回前 top 个频繁子图，默认为 10 - verbose?: boolean; -} - -const DEFAULT_LABEL_NAME = "cluster"; - -/** - * gSpan 频繁子图计算算法（frequent graph mining） - * @param params 参数 - */ -const gSpan = (params: Props): GraphData[] => { - // ------- 将图数据 GraphData 的 map 转换为格式 ------- - const { - graphs, - directed = false, - nodeLabelProp = DEFAULT_LABEL_NAME, - edgeLabelProp = DEFAULT_LABEL_NAME, - } = params; - const formattedGraphs = formatGraphs( - graphs, - directed, - nodeLabelProp, - edgeLabelProp - ); - const { minSupport, maxNodeNum, minNodeNum, verbose, top } = params; - - // ------- 初始化与执行算法 ------- - const algoParams = { - graphs: formattedGraphs, - minSupport, - maxNodeNum, - minNodeNum, - top, - verbose, - directed, - }; - const calculator = new GSpan(algoParams); - calculator.run(); - - const result = toGraphDatas( - calculator.frequentSubgraphs, - nodeLabelProp, - edgeLabelProp - ); - return result; -}; - -export default gSpan; diff --git a/packages/graph/src/gSpan/struct.ts b/packages/graph/src/gSpan/struct.ts deleted file mode 100644 index 03de226..0000000 --- a/packages/graph/src/gSpan/struct.ts +++ /dev/null @@ -1,114 +0,0 @@ -import { indexOf } from "@antv/util"; - -export const VACANT_EDGE_ID = -1; -export const VACANT_NODE_ID = -1; -export const VACANT_EDGE_LABEL = "-1"; -export const VACANT_NODE_LABEL = "-1"; -export const VACANT_GRAPH_ID = -1; -export const AUTO_EDGE_ID = "-1"; - -export class Edge { - public id: number; - public from: number; - public to: number; - public label: string; - - constructor( - id = VACANT_EDGE_ID, - from = VACANT_NODE_ID, - to = VACANT_NODE_ID, - label = VACANT_EDGE_LABEL - ) { - this.id = id; - this.from = from; - this.to = to; - this.label = label; - } -} - -export class Node { - public id: number; - public from: number; - public to: number; - public label: string; - public edges: Edge[]; - public edgeMap: {}; - - constructor(id = VACANT_NODE_ID, label = VACANT_NODE_LABEL) { - this.id = id; - this.label = label; - this.edges = []; - this.edgeMap = {}; - } - - addEdge(edge) { - this.edges.push(edge); - this.edgeMap[edge.id] = edge; - } -} - -export class Graph { - public id: number; - public from: number; - public to: number; - public label: string; - public edgeIdAutoIncrease: boolean; - public nodes: Node[]; - public edges: Edge[]; - public nodeMap: {}; - public edgeMap: {}; - public nodeLabelMap: {}; // key 是 label，value 是节点 id 的数组 - public edgeLabelMap: {}; - private counter: number; // 自增用于自动生成边 id - public directed: boolean; - - constructor( - id = VACANT_NODE_ID, - edgeIdAutoIncrease = true, - directed = false - ) { - this.id = id; - this.edgeIdAutoIncrease = edgeIdAutoIncrease; - this.edges = []; - this.nodes = []; - this.nodeMap = {}; - this.edgeMap = {}; - this.nodeLabelMap = {}; - this.edgeLabelMap = {}; - this.counter = 0; - this.directed = directed; - } - - getNodeNum() { - return this.nodes.length; - } - - addNode(id: number, label: string) { - if (this.nodeMap[id]) return; - const node = new Node(id, label); - this.nodes.push(node); - this.nodeMap[id] = node; - if (!this.nodeLabelMap[label]) this.nodeLabelMap[label] = []; - this.nodeLabelMap[label].push(id); - } - - addEdge(id: number, from: number, to: number, label: string) { - if (this.edgeIdAutoIncrease || id === undefined) id = this.counter++; - if (this.nodeMap[from] && this.nodeMap[to] && this.nodeMap[to].edgeMap[id]) - return; - const edge = new Edge(id, from, to, label); - this.edges.push(edge); - this.edgeMap[id] = edge; - - this.nodeMap[from].addEdge(edge); - - if (!this.edgeLabelMap[label]) this.edgeLabelMap[label] = []; - this.edgeLabelMap[label].push(edge); - - if (!this.directed) { - const rEdge = new Edge(id, to, from, label); - this.nodeMap[to].addEdge(rEdge); - this.edgeLabelMap[label].push(rEdge); - } - } -} diff --git a/packages/graph/src/gaddi.ts b/packages/graph/src/gaddi.ts deleted file mode 100644 index b9a61bf..0000000 --- a/packages/graph/src/gaddi.ts +++ /dev/null @@ -1,1211 +0,0 @@ -import floydWarshall from './floydWarshall'; -import { GraphData, Matrix } from './types'; -import gSpan, { EdgeMap, NodeMap } from './gSpan/gSpan'; -import dijkstra from './dijkstra'; -import { uniqueId } from './util'; - -/** 节点对 map */ -interface NodePairMap { - [key: string]: { - // key 的格式为 startNodeIdx-endNodeIdx - start: number; // 第一个节点的 idx - end: number; // 第二个节点的 idx - distance: number; // 两节点最短路径长度 - }; -} - -interface LabelMap { - [label: string]: any; -} - -/** 邻居单元类型 */ -interface NeighborUnit { - nodeId: string; - nodeIdx: number; - nodeIdxs: number[]; // the first one is nodeIdx - neighbors: any[]; // - neighborNum: number; - nodeLabelCountMap: { - [label: string]: { - count: number; - dists: number[]; // 按照从小到大排序的距离数组 - }; - }; -} - -/** 节点对的邻居交集的诱导子图 map */ -interface InterGraphMap { - [key: string]: GraphData; // key 格式由节点对的 idx 组成：beginIdx-endIdx，和 nodePairMap 对应 -} - -/** - * 为 graphData 中每个节点生成邻居单元数组 - * @param graphData - * @param spm - * @param nodeLabelProp - * @param k k-近邻 - */ -const findKNeighborUnits = ( - graphData: GraphData, - spm: Matrix[], - nodeLabelProp: string = 'cluster', - k: number = 2, -): NeighborUnit[] => { - const units: NeighborUnit[] = []; - const nodes = graphData.nodes; - spm.forEach((row: number[], i) => { - units.push(findKNeighborUnit(nodes, row, i, nodeLabelProp, k)); - }); - return units; -}; - -const findKNeighborUnit = (nodes, row, i, nodeLabelProp, k) => { - const unitNodeIdxs = [i]; - const neighbors = []; - const labelCountMap = {}; - row.forEach((v, j) => { - if (v <= k && i !== j) { - unitNodeIdxs.push(j); - neighbors.push(nodes[j]); - const label = nodes[j][nodeLabelProp]; - if (!labelCountMap[label]) labelCountMap[label] = { count: 1, dists: [v] }; - else { - labelCountMap[label].count++; - labelCountMap[label].dists.push(v); - } - } - }); - // 将 labelCountMap 中的 dists 按照从小到大排序，方便后面使用 - Object.keys(labelCountMap).forEach(label => { - labelCountMap[label].dists = labelCountMap[label].dists.sort((a, b) => a - b); - }); - return { - nodeIdx: i, - nodeId: nodes[i].id, - nodeIdxs: unitNodeIdxs, - neighbors, - neighborNum: unitNodeIdxs.length - 1, - nodeLabelCountMap: labelCountMap, - }; -}; - -/** - * 随机寻找点对，满足距离小于 k - * @param k 参数 k，表示 k-近邻 - * @param nodeNum 参数 length - * @param maxNodePairNum 寻找点对的数量不超过 maxNodePairNum - * @param spm 最短路径矩阵 - */ -const findNodePairsRandomly = ( - k: number, - nodeNum: number, - maxNodePairNum: number, - kNeighborUnits: NeighborUnit[], - spm: Matrix[], -): NodePairMap => { - // 每个节点需要随机找出的点对数 - let nodePairNumEachNode = Math.ceil(maxNodePairNum / nodeNum); - const nodePairMap = {}; - let foundNodePairCount = 0; - - // 遍历节点，为每个节点随机找出 nodePairNumEachNode 个点对，满足距离小于 k。找到的点对数量超过 maxNodePairNum 或所有节点遍历结束时终止 - kNeighborUnits.forEach((unit, i) => { - // 若未达到 nodePairNumEachNode，或循环次数小于最大循环次数(2 * nodeNum)，继续循环 - let nodePairForICount = 0; - let outerLoopCount = 0; - const neighbors = unit.nodeIdxs; // the first one is the center node - const neighborNum = unit.neighborNum - 1; - while (nodePairForICount < nodePairNumEachNode) { - // 另一端节点在节点数组中的的 index - let oidx = neighbors[1 + Math.floor(Math.random() * neighborNum)]; - let innerLoopCount = 0; - // 若随机得到的另一端 idx 不符合条件，则继续 random。条件是不是同一个节点、这个点对没有被记录过、距离小于 k - while (nodePairMap[`${i}-${oidx}`] || nodePairMap[`${oidx}-${i}`]) { - oidx = Math.floor(Math.random() * nodeNum); - innerLoopCount++; - if (innerLoopCount > 2 * nodeNum) break; // 循环次数大于最大循环次数(2 * nodeNum)跳出循环，避免死循环 - } - if (innerLoopCount < 2 * nodeNum) { - // 未达到最大循环次数，说明找到了合适的另一端 - nodePairMap[`${i}-${oidx}`] = { - start: i, - end: oidx, - distance: spm[i][oidx], - }; - nodePairForICount++; - foundNodePairCount++; - // 如果当前找到的点对数量达到了上限，返回结果 - if (foundNodePairCount >= maxNodePairNum) return nodePairMap; - } - outerLoopCount++; - if (outerLoopCount > 2 * nodeNum) break; // 循环次数大于最大循环次数(2 * nodeNum)跳出循环，避免死循环 - } - // 这个节点没有找到足够 nodePairNumEachNode 的点对。更新 nodePairNumEachNode，让后续节点找更多的点对 - if (nodePairForICount < nodePairNumEachNode) { - const gap = nodePairNumEachNode - nodePairForICount; - nodePairNumEachNode = (nodePairNumEachNode + gap) / (nodeNum - i - 1); - } - }); - return nodePairMap; -}; - -/** - * 计算所有 nodePairMap 中节点对的相交邻居诱导子图 - * @param nodePairMap 节点对 map，key 为 node1.id-node2.id，value 为 { startNodeIdx, endNodeIdx, distance } - * @param neighborUnits 每个节点的邻居元数组 - * @param graphData 原图数据 - * @param edgeMap 边的 map，方便检索 - * @param cachedInducedGraphMap 缓存的结果，下次进入该函数将继续更新该缓存，若 key 在缓存中存在则不需要重复计算 - */ -const getIntersectNeighborInducedGraph = ( - nodePairMap: NodePairMap, - neighborUnits: NeighborUnit[], - graphData: GraphData, - cachedInducedGraphMap?: InterGraphMap, -): InterGraphMap => { - const nodes = graphData.nodes; - if (!cachedInducedGraphMap) cachedInducedGraphMap = {}; - Object.keys(nodePairMap).forEach(key => { - if (cachedInducedGraphMap && cachedInducedGraphMap[key]) return; - cachedInducedGraphMap[key] = { nodes: [], edges: [] }; - const pair = nodePairMap[key]; - const startUnitNodeIds = neighborUnits[pair.start]?.nodeIdxs; - const endUnitNodeIds = neighborUnits[pair.end]?.nodeIdxs; - if (!startUnitNodeIds || !endUnitNodeIds) return; // 不存在邻元，返回空图 - const endSet = new Set(endUnitNodeIds); - const intersect = startUnitNodeIds.filter(x => endSet.has(x)); // 可能会爆栈（在 1580 + 6 nodes full-connected 时出现） - if (!intersect || !intersect.length) return; // 没有交集，返回空图 - const intersectIdMap = {}; - const intersectLength = intersect.length; - for (let i = 0; i < intersectLength; i++) { - const node = nodes[intersect[i]]; - cachedInducedGraphMap[key].nodes.push(node); // 将交集中的点加入诱导子图 - intersectIdMap[node.id] = true; - } - // 遍历所有边数据，如果边的两端都在交集中，将该边加入诱导子图 - graphData.edges.forEach(edge => { - if (intersectIdMap[edge.source] && intersectIdMap[edge.target]) - cachedInducedGraphMap[key].edges.push(edge); - }); - }); - return cachedInducedGraphMap; -}; - -/** - * 计算 strcutre 在 graph 上的匹配数量 - * @param graph 图数据 - * @param structure 目前支持只有两个节点一条边的最简单结构 - * @param nodeLabelProp 节点类型字段名 - * @param edgeLabelProp 边类型字段名 - */ -const getMatchedCount = (graph, structure, nodeLabelProp, edgeLabelProp) => { - const nodeMap = {}; - graph.nodes.forEach(node => { - nodeMap[node.id] = node; - }); - let count = 0; - if (!structure?.edges?.length || structure?.nodes?.length < 2) return 0; - graph.edges.forEach(e => { - const sourceLabel = nodeMap[e.source][nodeLabelProp]; - const targetLabel = nodeMap[e.target][nodeLabelProp]; - const strNodeLabel1 = structure?.nodes[0][nodeLabelProp]; - const strNodeLabel2 = structure?.nodes[1][nodeLabelProp]; - const strEdgeLabel = structure?.edges[0][edgeLabelProp]; - - if (e[edgeLabelProp] !== strEdgeLabel) return; - if ( - (sourceLabel === strNodeLabel1 && targetLabel === strNodeLabel2) || - (sourceLabel === strNodeLabel2 && targetLabel === strNodeLabel1) - ) { - count++; - } - }); - return count; -}; - -/** - * structures 中寻找最具有代表性的一个。这个结构是使得 matchedCountMap 的分组方式类内间距最小，类间间距最大 - * @param matchedCountMap 每个 structure 分类后的各图匹配数量，格式 { [strcture.idx]: { [interInducedGraphKey]: count } } - * @param structureNum strcuture 个数，与 matchedCountMap.length 对应 - * @param structures - */ -const findRepresentStructure = (matchedCountMap, structureNum, structures) => { - let maxOffset = Infinity, - representClusterType = 0; - for (let i = 0; i < structureNum; i++) { - // 一种分组的 map，key 是 intGraph 的 key，value 是 structures[i] 的匹配个数 - const countMapI = matchedCountMap[i]; - // 按照 value 为该组排序，生成 keys 的数组： - const sortedGraphKeys = Object.keys(countMapI).sort((a, b) => { - return countMapI[a] - countMapI[b]; - }); - - // 共 100 个 graphKeys，将 graphKeys 按顺序分为 groupNum 组 - const groupNum = 10; - const clusters = []; // 总共有 groupNum 个项 - sortedGraphKeys.forEach((key, j) => { - if (!clusters[j % groupNum]) - clusters[j % groupNum] = { graphs: [], totalCount: 0, aveCount: 0 }; - clusters[j % groupNum].graphs.push(key); - clusters[j % groupNum].totalCount += countMapI[key]; - }); - - // 计算 cluster 与 cluster 之间的距离 innerDist，每个 cluster 内部的距离 intraDist - let aveIntraDist = 0; // 该类的类内平均值 - const aveCounts = []; // 类内平均匹配数量，将用于计算类间距离 - clusters.forEach(graphsInCluster => { - // 类内均值 - const aveCount = graphsInCluster.totalCount / graphsInCluster.graphs.length; - graphsInCluster.aveCount = aveCount; - aveCounts.push(aveCount); - - // 对于每类，计算类内间距平均值 - let aveIntraPerCluster = 0; - const graphsNum = graphsInCluster.length; - graphsInCluster.graphs.forEach((graphKey1, j) => { - const graph1Count = countMapI[graphKey1]; - graphsInCluster.graphs.forEach((graphKey2, k) => { - if (j === k) return; - aveIntraPerCluster += Math.abs(graph1Count - countMapI[graphKey2]); - }); - }); - aveIntraPerCluster /= (graphsNum * (graphsNum - 1)) / 2; - aveIntraDist += aveIntraPerCluster; - }); - - aveIntraDist /= clusters.length; - - // 用类内均值计算类间距 - let aveInterDist = 0; // 类间间距平均值 - aveCounts.forEach((aveCount1, j) => { - aveCounts.forEach((aveCount2, k) => { - if (j === k) return; - aveInterDist += Math.abs(aveCount1 - aveCount2); - }); - aveInterDist /= (aveCounts.length * (aveCounts.length - 1)) / 2; - }); - - // 寻找 (类间间距均值-类内间距均值) 最大的一种分组方式（对应的 structure 就是最终要找的唯一 DS(G)） - const offset = aveInterDist - aveIntraDist; - if (maxOffset < offset) { - maxOffset = offset; - representClusterType = i; - } - } - return { - structure: structures[representClusterType], - structureCountMap: matchedCountMap[representClusterType], - }; -}; - -const getNodeMaps = (nodes, nodeLabelProp): { nodeMap: NodeMap; nodeLabelMap: LabelMap } => { - const nodeMap: NodeMap = {}, - nodeLabelMap: LabelMap = {}; - nodes.forEach((node, i) => { - nodeMap[node.id] = { idx: i, node, degree: 0, inDegree: 0, outDegree: 0 }; - const label = node[nodeLabelProp]; - if (!nodeLabelMap[label]) nodeLabelMap[label] = []; - nodeLabelMap[label].push(node); - }); - return { nodeMap, nodeLabelMap }; -}; - -const getEdgeMaps = ( - edges, - edgeLabelProp, - nodeMap: NodeMap, -): { edgeMap: EdgeMap; edgeLabelMap: LabelMap } => { - const edgeMap = {}, - edgeLabelMap = {}; - edges.forEach((edge, i) => { - edgeMap[`${uniqueId}`] = { idx: i, edge }; - const label = edge[edgeLabelProp]; - if (!edgeLabelMap[label]) edgeLabelMap[label] = []; - edgeLabelMap[label].push(edge); - - const sourceNode = nodeMap[edge.source]; - if (sourceNode) { - sourceNode.degree++; - sourceNode.outDegree++; - } - const targetNode = nodeMap[edge.target]; - if (targetNode) { - targetNode.degree++; - targetNode.inDegree++; - } - }); - return { edgeMap, edgeLabelMap }; -}; - -/** - * 输出最短路径的 map，key 为 sourceNode.id-targetNode.id，value 为这两个节点的最短路径长度 - * @param nodes - * @param spm - * @param directed - */ -const getSpmMap = (nodes, spm, directed): { [key: string]: number } => { - const length = spm.length; - const map = {}; - spm.forEach((row, i) => { - const start = directed ? 0 : i + 1; - const iId = nodes[i].id; - for (let j = start; j < length; j++) { - if (i === j) continue; - const jId = nodes[j].id; - const dist = row[j]; - map[`${iId}-${jId}`] = dist; - if (!directed) map[`${jId}-${iId}`] = dist; - } - }); - return map; -}; - -/** - * 计算一对节点（node1，node2）的 NDS 距离 - * @param graph 原图数据 - * @param node1 - * @param node2 - */ -const getNDSDist = ( - graph, - node1, - node2, - nodeMap, - spDist, - kNeighborUnits, - structure, - nodeLabelProp, - edgeLabelProp, - cachedNDSMap, - cachedInterInducedGraph, -) => { - const key = `${node1.id}-${node2.id}`; - if (cachedNDSMap && cachedNDSMap[key]) return cachedNDSMap[key]; - let interInducedGraph = cachedInterInducedGraph ? cachedInterInducedGraph[key] : undefined; - // 若没有缓存相交邻居诱导子图，计算 - if (!interInducedGraph) { - const pairMap: NodePairMap = { - [key]: { - start: nodeMap[node1.id].idx, - end: nodeMap[node2.id].idx, - distance: spDist, - }, - }; - - cachedInterInducedGraph = getIntersectNeighborInducedGraph( - pairMap, - kNeighborUnits, - graph, - cachedInterInducedGraph, - ); - interInducedGraph = cachedInterInducedGraph[key]; - } - - return getMatchedCount(interInducedGraph, structure, nodeLabelProp, edgeLabelProp); -}; - -/** - * 计算 pattern 上绩点的度数并存储到 minPatternNodeLabelDegreeMap - */ -const stashPatternNodeLabelDegreeMap = (minPatternNodeLabelDegreeMap, neighborLabel, patternNodeMap, patternNodeLabelMap) => { - let minPatternNodeLabelDegree = minPatternNodeLabelDegreeMap[neighborLabel]?.degree; - let minPatternNodeLabelInDegree = minPatternNodeLabelDegreeMap[neighborLabel]?.inDegree; - let minPatternNodeLabelOutDegree = minPatternNodeLabelDegreeMap[neighborLabel]?.outDegree; - - if (minPatternNodeLabelDegreeMap[neighborLabel] === undefined) { - minPatternNodeLabelDegree = Infinity; - minPatternNodeLabelInDegree = Infinity; - minPatternNodeLabelOutDegree = Infinity; - patternNodeLabelMap[neighborLabel].forEach(patternNodeWithLabel => { - const patternNodeDegree = patternNodeMap[patternNodeWithLabel.id].degree; - if (minPatternNodeLabelDegree > patternNodeDegree) - minPatternNodeLabelDegree = patternNodeDegree; - const patternNodeInDegree = patternNodeMap[patternNodeWithLabel.id].inDegree; - if (minPatternNodeLabelInDegree > patternNodeInDegree) - minPatternNodeLabelInDegree = patternNodeInDegree; - const patternNodeOutDegree = patternNodeMap[patternNodeWithLabel.id].outDegree; - if (minPatternNodeLabelOutDegree > patternNodeOutDegree) - minPatternNodeLabelOutDegree = patternNodeOutDegree; - }); - minPatternNodeLabelDegreeMap[neighborLabel] = { - degree: minPatternNodeLabelDegree, - inDegree: minPatternNodeLabelInDegree, - outDegree: minPatternNodeLabelOutDegree - }; - } - - return { - minPatternNodeLabelDegree, - minPatternNodeLabelInDegree, - minPatternNodeLabelOutDegree - } -} - -/** - * GADDI 模式匹配 - * @param graphData 原图数据 - * @param pattern 搜索图（需要在原图上搜索的模式）数据 - * @param directed 是否计算有向图，默认 false - * @param k 参数 k，表示 k-近邻 - * @param length 参数 length - * @param nodeLabelProp 节点数据中代表节点标签（分类信息）的属性名。默认为 cluster - * @param edgeLabelProp 边数据中代表边标签（分类信息）的属性名。默认为 cluster - */ -const GADDI = ( - graphData: GraphData, - pattern: GraphData, - directed: boolean = false, - k: number, - length: number, - nodeLabelProp: string = 'cluster', - edgeLabelProp: string = 'cluster', -): GraphData[] => { - if (!graphData || !graphData.nodes) return; - // 分为三步： - // 0. 预计算：节点/边数，邻接矩阵、最短路径矩阵 - // 1. 处理原图 graphData。再分为 1~5 小步 - // 2. 匹配 - - // console.log("----- stage-pre: preprocessing -------"); - - // -------- 第零步，预计算：节点/边数，邻接矩阵、最短路径矩阵------- - const nodeNum = graphData.nodes.length; - if (!nodeNum) return; - // console.log("----- stage-pre.1: calc shortest path matrix for graph -------"); - const spm = floydWarshall(graphData, directed); - // console.log( - // "----- stage-pre.2: calc shortest path matrix for pattern -------" - // ); - const patternSpm = floydWarshall(pattern, directed); - // console.log( - // "----- stage-pre.3: calc shortest path matrix map for graph -------" - // ); - const spmMap = getSpmMap(graphData.nodes, spm, directed); - // console.log( - // "----- stage-pre.4: calc shortest path matrix map for pattern -------" - // ); - const patternSpmMap = getSpmMap(pattern.nodes, patternSpm, directed); - - // console.log("----- stage-pre.5: establish maps -------"); - // 节点的 map，以 id 为 id 映射，方便后续快速检索 - const { nodeMap, nodeLabelMap } = getNodeMaps(graphData.nodes, nodeLabelProp); - const { nodeMap: patternNodeMap, nodeLabelMap: patternNodeLabelMap } = getNodeMaps( - pattern.nodes, - nodeLabelProp, - ); - - // 计算节点度数 - getEdgeMaps(graphData.edges, edgeLabelProp, nodeMap); - - const { edgeLabelMap: patternEdgeLabelMap } = getEdgeMaps( - pattern.edges, - edgeLabelProp, - patternNodeMap, - ); - - // 若未指定 length，自动计算 pattern 半径（最短路径最大值） - let patternSpmSpread = []; - patternSpm?.forEach(row => { - patternSpmSpread = patternSpmSpread.concat(row); - }) - if (!length) length = Math.max(...patternSpmSpread, 2); - if (!k) k = length; - - // console.log("params", directed, length, k); - - // console.log("----- stage-pre.6: calc k neighbor units -------"); - // 计算每个节点的 k 邻元集合 - const kNeighborUnits = findKNeighborUnits(graphData, spm, nodeLabelProp, k); - const patternKNeighborUnits = findKNeighborUnits(pattern, patternSpm, nodeLabelProp, k); - - // console.log( - // "----- stage0: going to processing graph and find intersect neighbor induced graphs -------" - // ); - - // console.log("----- stage0.1: going to select random node pairs -------"); - // -------- 第一步，处理原图 graphData------- - - // 1.1. 随机选择最多 100 个点对，满足距离小于 Length 和 k - // 当 graphData 少于 20 个节点，则不能找出 100 个点对，只找出不多于 n(n-1)/2 个点对 - const maxNodePairNum = Math.min(100, (nodeNum * (nodeNum - 1)) / 2); - const nodePairsMap = findNodePairsRandomly( - k, - nodeNum, - maxNodePairNum, - kNeighborUnits, - spm, - ); - - // console.log( - // "----- stage0.2: going to calculate intersect neighbor induced graphs -------" - // ); - // 1.2. 生成上面节点对的相应相交邻居诱导子图。格式为 {'beginNodeIdx-endNodeIdx': {nodes: [], edges: []}} - let intGMap = getIntersectNeighborInducedGraph(nodePairsMap, kNeighborUnits, graphData); - // 1.3. 使用 gSpan 算法（frequent graph mining）计算 ISIntG 的前 10 个频率最高的子结构（3-4条边） - const top = 10, - minSupport = 1, - minNodeNum = 1, - maxNodeNum = 4; - const params = { - graphs: intGMap, - nodeLabelProp, - edgeLabelProp, - minSupport, - minNodeNum, - maxNodeNum, - directed, - }; - - // console.log( - // "----- stage1: (gSpan) going to find frequent structure dsG -------" - // ); - // console.log("----- stage1.1: going to run gSpan -------"); - // 暂时假设生成的 sub structure 都只有一条边 - const freStructures = gSpan(params).slice(0, top); - // structureNum 可能小于 top - const structureNum = freStructures.length; - - // 1.4. 计算上述 10 个子结构在 intGMap 中每个诱导子图的匹配个数 - const matchedCountMap = []; - freStructures.forEach((structure, i) => { - matchedCountMap[i] = {}; - Object.keys(intGMap).forEach(key => { - const graph = intGMap[key]; - const subStructureCount = getMatchedCount(graph, structure, nodeLabelProp, edgeLabelProp); - matchedCountMap[i][key] = subStructureCount; - }); - }); - - // console.log( - // "----- stage1.1: going to find the most represent strucutre -------" - // ); - - // 1.5. 对于每个子结构，根据匹配个数为 intGMap 中的诱导子图分组，生成 structureNum 种分组 - // 计算每种分组的类间距和类内间距，找到类间距最大、类内间距最小的一种分组，这种分组对应的子结构被选为唯一代表性子结构 DS(G) - const { structure: dsG, structureCountMap: ndsDist } = findRepresentStructure( - matchedCountMap, - structureNum, - freStructures, - ); - - // -------- 第二步，匹配------- - // 2.1 找到从 Q 中的一个节点作为起始节点，寻找 G 中的匹配。这个其实节点的标签可以在 G 中找到最多的节点 - let beginPNode = pattern.nodes[0], - candidates = [], - label = pattern.nodes[0]?.[nodeLabelProp], - maxNodeNumWithSameLabel = -Infinity; - pattern.nodes.forEach(node => { - const pLabel = node[nodeLabelProp]; - const nodesWithSameLabel = nodeLabelMap[pLabel] - if (nodesWithSameLabel?.length > maxNodeNumWithSameLabel) { - maxNodeNumWithSameLabel = nodesWithSameLabel.length; - candidates = nodesWithSameLabel; - label = pLabel; - beginPNode = node; - } - }); - - // console.log("----- stage2: going to find candidates -------"); - - // 全局缓存，避免重复计算 - const minPatternNodeLabelDegreeMap = {}; // key 是 label，value 是该 label 节点的最小度数 - let patternIntGraphMap = {}, - patternNDSDist = {}, // key 为 node.id-node.id - patternNDSDistMap = {}; // key 为 node.id-label2，value nds距离值数组（按从大到小排序，无需关心具体对应哪个 node2） - // 2.2.2 对于 Q 中的另一个标签的 k 个节点，计算它们到 node 的最短路径以及 NDS 距离 - const patternSpDist = {}; - const patternSpDistBack = {}; - Object.keys(patternNodeLabelMap).forEach((label2, j) => { - patternSpDist[label2] = []; - if (directed) { - patternSpDistBack[label2] = []; - } - let maxDist = -Infinity; - const patternNodesWithLabel2 = patternNodeLabelMap[label2]; - const patternNodePairMap = {}; - patternNodesWithLabel2.forEach(nodeWithLabel2 => { - const dist = patternSpmMap[`${beginPNode.id}-${nodeWithLabel2.id}`]; - dist && patternSpDist[label2].push(dist); - if (maxDist < dist) maxDist = dist; - patternNodePairMap[`${beginPNode.id}-${nodeWithLabel2.id}`] = { - start: 0, - end: patternNodeMap[nodeWithLabel2.id].idx, - distance: dist, - }; - if (directed) { - const distBack = patternSpmMap[`${nodeWithLabel2.id}-${beginPNode.id}`]; - distBack && patternSpDistBack[label2].push(distBack); - } - }); - - // spDist[label2] 按照从小到大排序 - patternSpDist[label2] = patternSpDist[label2].sort((a, b) => a - b); - if (directed) patternSpDistBack[label2] = patternSpDistBack[label2].sort((a, b) => a - b); - - // 计算 Q 中所有 label2 节点到 beginPNode 的 NDS 距离 - // 所有 label2 节点到 beginPNode 的邻居相交诱导子图： - // key: node1.id-node2.id - patternIntGraphMap = getIntersectNeighborInducedGraph( - patternNodePairMap, - patternKNeighborUnits, - pattern, - patternIntGraphMap, - ); - // pattern 中 beginNode 到当前 label2 节点 的 NDS 距离（数组，无需关心具体对应到哪个节点） - let currentPatternNDSDistArray = []; - Object.keys(patternNodePairMap).forEach(key => { - if (patternNDSDist[key]) { - currentPatternNDSDistArray.push(patternNDSDist[key]); - return; // 缓存过则不需要再次计算 - } - const patternIntGraph = patternIntGraphMap[key]; - patternNDSDist[key] = getMatchedCount(patternIntGraph, dsG, nodeLabelProp, edgeLabelProp); - currentPatternNDSDistArray.push(patternNDSDist[key]); - }); - - // 根据值为 currentPatternNDSDist 从大到小排序 - currentPatternNDSDistArray = currentPatternNDSDistArray.sort((a, b) => b - a); - patternNDSDistMap[`${beginPNode.id}-${label2}`] = currentPatternNDSDistArray; - - if (label2 === label) return; - - const candidatesNum = candidates?.length || 0; - for (let m = candidatesNum - 1; m >= 0; m--) { - const cNode = candidates[m]; - - // prune1：若 candidates 中节点 cNode 的 kNeighborUnits 中标签为 label2 的节点个数少于 pattern 中 label2 个数，删去它 - const graphNeighborUnit = kNeighborUnits[nodeMap[cNode.id].idx]; - const graphNeighborUnitCountMap = graphNeighborUnit.nodeLabelCountMap[label2]; - const patternLabel2Num = patternNodeLabelMap[label2].length; - if (!graphNeighborUnitCountMap || graphNeighborUnitCountMap.count < patternLabel2Num) { - candidates.splice(m, 1); - continue; - } - - // prune2：若 candidates 中节点 cNode 到 kNeighborUnits 中标签为 label2 的节点最短路径大于 patternSpDist[label2]，删去它 - // (prune2 规则即：candidate 相关的最短路径的最大 spDist[label2].length 个，按照大小顺序依次和 patternSpDist[label2] 中的值比较，只要遇到一个是 G > Q 的，就删去这个 candidate) - let prune2Invalid = false; - for (let n = 0; n < patternLabel2Num; n++) { - if (graphNeighborUnitCountMap.dists[n] > patternSpDist[label2][n]) { - prune2Invalid = true; - break; - } - } - if (prune2Invalid) { - candidates.splice(m, 1); - continue; - } - - // prune3：若 candidates 中节点 cNode 到 kNeighborUnits 中标签为 label2 的节点 NDS 距离小于 patternNDSDist[beginNode.id-label2]，删去它 - // TODO：prune3，currentPatternNDSDistArray 与 currentNDSDist 的比较 - - // 计算 G 中所有 label2 节点到 cNode 的 NDS 距离 - // 所有 label2 节点到 cNode 的邻居相交诱导子图： - const cNodePairMap = {}; - graphNeighborUnit.neighbors.forEach(neighborNode => { - const dist = spmMap[`${cNode.id}-${neighborNode.id}`]; - cNodePairMap[`${cNode.id}-${neighborNode.id}`] = { - start: nodeMap[cNode.id].idx, - end: nodeMap[neighborNode.id].idx, - distance: dist, - }; - }); - // 更新 intGMap - intGMap = getIntersectNeighborInducedGraph(cNodePairMap, kNeighborUnits, graphData, intGMap); - // candidate 到它周围 label2 节点的 NDS 距离, key 是 node.id-node.id - let currentNDSDistArray = []; - Object.keys(cNodePairMap).forEach(key => { - if (ndsDist[key]) { - currentNDSDistArray.push(ndsDist[key]); - return; // 缓存过则不需要再次计算 - } - const intGraph = intGMap[key]; - ndsDist[key] = getMatchedCount(intGraph, dsG, nodeLabelProp, edgeLabelProp); - currentNDSDistArray.push(ndsDist[key]); - }); - - // 根据值为 currentNDSDistArray 从大到小排序 - currentNDSDistArray = currentNDSDistArray.sort((a, b) => b - a); - - let prune3Invalid = false; - for (let n = 0; n < patternLabel2Num; n++) { - if (currentNDSDistArray[n] < currentPatternNDSDistArray[n]) { - prune3Invalid = true; - break; - } - } - if (prune3Invalid) { - candidates.splice(m, 1); - continue; - } - } - }); - - const candidateGraphs = []; - - // console.log( - // "----- stage3: going to splice neighbors for each candidate graph -------" - // ); - - // candidates 经过筛选后，以每个 candidate 为中心，生成 Length-neighbor 的邻居诱导子图 - // 并在诱导子图中去除不可能在 Q 上找到匹配的点：在 Q 上不存在的 label，其他 label 到 candidate 的最大最短距离符合 Q、NDS 距离符合 Q - candidates?.forEach(candidate => { - const nodeIdx = nodeMap[candidate.id].idx; - const lengthNeighborUnit = findKNeighborUnit( - graphData.nodes, - spm[nodeIdx], - nodeIdx, - nodeLabelProp, - length, - ); - - const neighborNodes = lengthNeighborUnit.neighbors; - - // 删除不可能找到匹配的邻居点 - const neighborNum = neighborNodes.length; - let unmatched = false; - for (let i = neighborNum - 1; i >= 0; i--) { - // 如果通过裁剪，符合条件的节点数量已过少，说明不能匹配这个 candidate 相关的图 - if (neighborNodes.length + 1 < pattern.nodes.length) { - unmatched = true; - return; - } - const neighborNode = neighborNodes[i]; - const neighborLabel = neighborNode[nodeLabelProp]; - // prune1: 若该邻居点的 label 不存在于 pattern 中，移除这个点 - if (!patternNodeLabelMap[neighborLabel] || !patternNodeLabelMap[neighborLabel].length) { - neighborNodes.splice(i, 1); - continue; - } - - // prune2: 若该邻居点到 candidate 的最短路径比和它有相同 label 的节点到 beginPNode 的最大最短路径长度长，移除这个点 - // prune2.1: 如果没有这个标签到 beginPNode 的距离记录，说明 pattern 上（可能 beginPNode 是这个 label）没有其他这个 label 的节点 - if (!patternSpDist[neighborLabel] || !patternSpDist[neighborLabel].length) { - neighborNodes.splice(i, 1); - continue; - } - - const key = `${candidate.id}-${neighborNode.id}`; - - // prune2.2 - const distToCandidate = spmMap[key]; - let idx = patternSpDist[neighborLabel].length - 1; - let maxDistWithLabelInPattern = patternSpDist[neighborLabel][idx]; // patternSpDist[neighborLabel] 已经按照从小到大排序 - if (distToCandidate > maxDistWithLabelInPattern) { - neighborNodes.splice(i, 1); - continue; - } - - if (directed) { - const keyBack = `${neighborNode.id}-${candidate.id}`; - const distFromCandidate = spmMap[keyBack]; - idx = patternSpDistBack[neighborLabel].length - 1; - let maxBackDistWithLabelInPattern = patternSpDistBack[neighborLabel][idx]; - if (distFromCandidate > maxBackDistWithLabelInPattern) { - neighborNodes.splice(i, 1); - continue; - } - } - - // prune3: 若该邻居点到 candidate 的 NDS 距离比和它有相同 label 的节点到 beginPNode 的最小 NDS 距离小，移除这个点 - const ndsToCandidate = ndsDist[key] - ? ndsDist[key] - : getNDSDist( - graphData, - candidate, - neighborNode, - nodeMap, - distToCandidate, - kNeighborUnits, - dsG, - nodeLabelProp, - edgeLabelProp, - ndsDist, - intGMap, - ); - const patternKey = `${beginPNode.id}-${neighborLabel}`; - const minNdsWithLabelInPattern = - patternNDSDistMap[patternKey][patternNDSDistMap[patternKey].length - 1]; // patternNDSDist[key] 一定存在 - if (ndsToCandidate < minNdsWithLabelInPattern) { - neighborNodes.splice(i, 1); - continue; - } - - // prune4: 若该邻居点的度数小于 pattern 同 label 节点最小度数，删去该点 - const { - minPatternNodeLabelDegree, - minPatternNodeLabelInDegree, - minPatternNodeLabelOutDegree - } = stashPatternNodeLabelDegreeMap(minPatternNodeLabelDegreeMap, neighborLabel, patternNodeMap,patternNodeLabelMap); - - if (nodeMap[neighborNode.id].degree < minPatternNodeLabelDegree) { - neighborNodes.splice(i, 1); - continue; - } - } - - // 节点在个数上符合匹配（不少于 pattern 的节点个数），现在筛选相关边 - if (!unmatched) { - candidateGraphs.push({ - nodes: [candidate].concat(neighborNodes), - }); - } - }); - - // console.log( - // "----- stage4: going to splice edges and neighbors for each candidate graph -------" - // ); - - const { length: undirectedLengthsToBeginPNode } = dijkstra(pattern, beginPNode.id, false); - - let undirectedLengthsToBeginPNodeLabelMap = {}; - if (directed) { - Object.keys(undirectedLengthsToBeginPNode).forEach(nodeId => { - const nodeLabel = patternNodeMap[nodeId].node[nodeLabelProp]; - if (!undirectedLengthsToBeginPNodeLabelMap[nodeLabel]) - undirectedLengthsToBeginPNodeLabelMap[nodeLabel] = [undirectedLengthsToBeginPNode[nodeId]]; - else - undirectedLengthsToBeginPNodeLabelMap[nodeLabel].push( - undirectedLengthsToBeginPNode[nodeId], - ); - }); - Object.keys(undirectedLengthsToBeginPNodeLabelMap).forEach(pLabel => { - undirectedLengthsToBeginPNodeLabelMap[pLabel].sort((a, b) => a - b); - }); - } else { - undirectedLengthsToBeginPNodeLabelMap = patternSpDist; - } - - // 现在 candidateGraphs 里面只有节点，进行边的筛选 - let candidateGraphNum = candidateGraphs.length; - for (let i = candidateGraphNum - 1; i >= 0; i--) { - const candidateGraph = candidateGraphs[i]; - const candidate = candidateGraph.nodes[0]; - - const candidateNodeLabelCountMap = {}; - const candidateNodeMap = {}; - candidateGraph.nodes.forEach((node, q) => { - candidateNodeMap[node.id] = { - idx: q, - node, - degree: 0, - inDegree: 0, - outDegree: 0 - }; - const cNodeLabel = node[nodeLabelProp]; - if (!candidateNodeLabelCountMap[cNodeLabel]) candidateNodeLabelCountMap[cNodeLabel] = 1; - else candidateNodeLabelCountMap[cNodeLabel]++; - }); - - // 根据 candidate 和 neighborNodes 中的节点生成 G 的诱导子图 - // 即，将 graphData 上两端都在 candidateGraph.nodes 中的边放入 candidateEdges - const candidateEdges = []; - const edgeLabelCountMap = {}; - graphData.edges.forEach(edge => { - if (candidateNodeMap[edge.source] && candidateNodeMap[edge.target]) { - candidateEdges.push(edge); - if (!edgeLabelCountMap[edge[edgeLabelProp]]) edgeLabelCountMap[edge[edgeLabelProp]] = 1; - else edgeLabelCountMap[edge[edgeLabelProp]]++; - candidateNodeMap[edge.source].degree++; - candidateNodeMap[edge.target].degree++; - candidateNodeMap[edge.source].outDegree++; - candidateNodeMap[edge.target].inDegree++; - } - }); - - // prune：若有一个 edgeLabel 在 candidateGraph 上的个数少于 pattern，去除该图 - const pattenrEdgeLabelNum = Object.keys(patternEdgeLabelMap).length; - let prunedByEdgeLabel = false; - for (let e = 0; e < pattenrEdgeLabelNum; e++) { - const label = Object.keys(patternEdgeLabelMap)[e]; - if ( - !edgeLabelCountMap[label] || - edgeLabelCountMap[label] < patternEdgeLabelMap[label].length - ) { - prunedByEdgeLabel = true; - break; - } - } - if (prunedByEdgeLabel) { - candidateGraphs.splice(i, 1); - continue; - } - - // 遍历 candidateEdges，进行边的筛选 - let candidateEdgeNum = candidateEdges.length; - - // prune：若边数过少，去除该图 - if (candidateEdgeNum < pattern.edges.length) { - candidateGraphs.splice(i, 1); - break; - } - let candidateGraphInvalid = false; - for (let e = candidateEdgeNum - 1; e >= 0; e--) { - const edge = candidateEdges[e]; - const edgeLabel = edge[edgeLabelProp]; - const patternEdgesWithLabel = patternEdgeLabelMap[edgeLabel]; - - // prune 1: 若边的 label 不存在于 pattern 边 label 中，去除该边 - if (!patternEdgesWithLabel || !patternEdgesWithLabel.length) { - edgeLabelCountMap[edgeLabel]--; - // 若这个 label 的 count 减少之后，该 label 的边数不足，去除该图 - if (patternEdgesWithLabel && edgeLabelCountMap[edgeLabel] < patternEdgesWithLabel.length) { - candidateGraphInvalid = true; - break; - } - candidateEdges.splice(e, 1); - candidateNodeMap[edge.source].degree--; - candidateNodeMap[edge.target].degree--; - candidateNodeMap[edge.source].outDegree--; - candidateNodeMap[edge.target].inDegree--; - continue; - } - - // prune 2: 若边的 label +两端 label 的三元组关系不能在 pattern 中找到，去除该边 - const sourceLabel = candidateNodeMap[edge.source].node[nodeLabelProp]; - const targetLabel = candidateNodeMap[edge.target].node[nodeLabelProp]; - - let edgeMatched = false; - patternEdgesWithLabel.forEach(patternEdge => { - const patternSource = patternNodeMap[patternEdge.source].node; - const patternTarget = patternNodeMap[patternEdge.target].node; - if ( - patternSource[nodeLabelProp] === sourceLabel && - patternTarget[nodeLabelProp] === targetLabel - ) - edgeMatched = true; - if ( - !directed && - patternSource[nodeLabelProp] === targetLabel && - patternTarget[nodeLabelProp] === sourceLabel - ) - edgeMatched = true; - }); - if (!edgeMatched) { - edgeLabelCountMap[edgeLabel]--; - // 若这个 label 的 count 减少之后，该 label 的边数不足，去除该图 - if (patternEdgesWithLabel && edgeLabelCountMap[edgeLabel] < patternEdgesWithLabel.length) { - candidateGraphInvalid = true; - break; - } - candidateEdges.splice(e, 1); - candidateNodeMap[edge.source].degree--; - candidateNodeMap[edge.target].degree--; - candidateNodeMap[edge.source].outDegree--; - candidateNodeMap[edge.target].inDegree--; - continue; - } - } - - // prune2: 删除边的过程中，发现边数过少/边 label 数过少时，去除该图 - if (candidateGraphInvalid) { - candidateGraphs.splice(i, 1); - continue; - } - - candidateGraph.edges = candidateEdges; - - const { length: lengthsToCandidate } = dijkstra( - candidateGraph, - candidateGraph.nodes[0].id, - false, // 此处计算路径长度用于判断是否连通，因此使用无向图 - ); - Object.keys(lengthsToCandidate) - .reverse() - .forEach(targetId => { - if (targetId === candidateGraph.nodes[0].id || candidateGraphInvalid) return; - // prune4: 通过上述裁剪，可能导致该邻居子图变为不连通。裁剪掉目前在这个邻居子图中和 candidate（第一个节点）不连通的节点 - if (lengthsToCandidate[targetId] === Infinity) { - const targetNodeLabel = candidateNodeMap[targetId].node[nodeLabelProp]; - candidateNodeLabelCountMap[targetNodeLabel]--; - if ( - candidateNodeLabelCountMap[targetNodeLabel] < - patternNodeLabelMap[targetNodeLabel].length - ) { - candidateGraphInvalid = true; - return; - } - const idx = candidateGraph.nodes.indexOf(candidateNodeMap[targetId].node); - candidateGraph.nodes.splice(idx, 1); - candidateNodeMap[targetId] = undefined; - return; - } - // prune5: 经过边裁剪后，可能又出现了最短路径过长的节点 （比 pattern 中同 label 的节点到 beginNode 最大最短距离远），删去这些节点 - const nLabel = nodeMap[targetId].node[nodeLabelProp]; - if ( - !undirectedLengthsToBeginPNodeLabelMap[nLabel] || - !undirectedLengthsToBeginPNodeLabelMap[nLabel].length || - lengthsToCandidate[targetId] > - undirectedLengthsToBeginPNodeLabelMap[nLabel][ - undirectedLengthsToBeginPNodeLabelMap[nLabel].length - 1 - ] - ) { - const targetNodeLabel = candidateNodeMap[targetId].node[nodeLabelProp]; - candidateNodeLabelCountMap[targetNodeLabel]--; - if ( - candidateNodeLabelCountMap[targetNodeLabel] < - patternNodeLabelMap[targetNodeLabel].length - ) { - candidateGraphInvalid = true; - return; - } - const idx = candidateGraph.nodes.indexOf(candidateNodeMap[targetId].node); - candidateGraph.nodes.splice(idx, 1); - candidateNodeMap[targetId] = undefined; - } - }); - - if (candidateGraphInvalid) { - candidateGraphs.splice(i, 1); - continue; - } - - let degreeChanged = true; - let loopCount = 0; - while (degreeChanged && !candidateGraphInvalid) { - degreeChanged = false; - - // candidate 度数不足，删去该图 - const condition = directed ? (candidateNodeMap[candidate.id].degree < patternNodeMap[beginPNode.id].degree || - candidateNodeMap[candidate.id].inDegree < patternNodeMap[beginPNode.id].inDegree || - candidateNodeMap[candidate.id].outDegree < patternNodeMap[beginPNode.id].outDegree) : - candidateNodeMap[candidate.id].degree < patternNodeMap[beginPNode.id].degree; - if (condition) { - candidateGraphInvalid = true; - break; - } - // candidate label 个数不足，删去该图 - if ( - candidateNodeLabelCountMap[candidate[nodeLabelProp]] < - patternNodeLabelMap[candidate[nodeLabelProp]].length - ) { - candidateGraphInvalid = true; - break; - } - - // prune6：去除度数过小的节点 - const currentCandidateNodeNum = candidateGraph.nodes.length; - for (let o = currentCandidateNodeNum - 1; o >= 0; o--) { - const cgNode = candidateGraph.nodes[o]; - const nodeDegree = candidateNodeMap[cgNode.id].degree; - const nodeInDegree = candidateNodeMap[cgNode.id].inDegree; - const nodeOutDegree = candidateNodeMap[cgNode.id].outDegree; - const cNodeLabel = cgNode[nodeLabelProp]; - - const { - minPatternNodeLabelDegree, - minPatternNodeLabelInDegree, - minPatternNodeLabelOutDegree - } = stashPatternNodeLabelDegreeMap(minPatternNodeLabelDegreeMap, cNodeLabel, patternNodeMap,patternNodeLabelMap); - - const deleteCondition = directed ? (nodeDegree < minPatternNodeLabelDegree || - nodeInDegree < minPatternNodeLabelInDegree || - nodeOutDegree < minPatternNodeLabelOutDegree) : - nodeDegree < minPatternNodeLabelDegree; - if (deleteCondition) { - candidateNodeLabelCountMap[cgNode[nodeLabelProp]]--; - // 节点 label 个数不足 - if ( - candidateNodeLabelCountMap[cgNode[nodeLabelProp]] < - patternNodeLabelMap[cgNode[nodeLabelProp]].length - ) { - candidateGraphInvalid = true; - break; - } - candidateGraph.nodes.splice(o, 1); - candidateNodeMap[cgNode.id] = undefined; - degreeChanged = true; - } - } - if (candidateGraphInvalid || (!degreeChanged && loopCount !== 0)) break; - // 经过 prune5 节点裁剪，删去端点已经不在 candidateGraph 中的边 - candidateEdgeNum = candidateEdges.length; - for (let y = candidateEdgeNum - 1; y >= 0; y--) { - const cedge = candidateEdges[y]; - if (!candidateNodeMap[cedge.source] || !candidateNodeMap[cedge.target]) { - candidateEdges.splice(y, 1); - const edgeLabel = cedge[edgeLabelProp]; - edgeLabelCountMap[edgeLabel]--; - if (candidateNodeMap[cedge.source]) { - candidateNodeMap[cedge.source].degree--; - candidateNodeMap[cedge.source].outDegree--; - } - if (candidateNodeMap[cedge.target]) { - candidateNodeMap[cedge.target].degree--; - candidateNodeMap[cedge.target].inDegree--; - } - // 边 label 数量不足 - if ( - patternEdgeLabelMap[edgeLabel] && - edgeLabelCountMap[edgeLabel] < patternEdgeLabelMap[edgeLabel].length - ) { - candidateGraphInvalid = true; - break; - } - degreeChanged = true; - } - } - loopCount++; - } - - if (candidateGraphInvalid) { - candidateGraphs.splice(i, 1); - continue; - } - - // prune: 若节点/边数过少，节点/边 label 过少，去掉这个图 - if ( - candidateGraphInvalid || - candidateGraph.nodes.length < pattern.nodes.length || - candidateEdges.length < pattern.edges.length - ) { - candidateGraphs.splice(i, 1); - continue; - } - } - - // 此时已经生成的多个 candidateGraphs，可能有重复 - - // console.log( - // "----- stage5: going to splice dulplicated candidate graphs -------" - // ); - - // 删去 candidateGraphs 中一模一样的子图，通过边的 node-node-edgeLabel 作为 key，这类边个数作为 value，进行匹配 - let currentLength = candidateGraphs.length; - for (let i = 0; i <= currentLength - 1; i++) { - const cg1 = candidateGraphs[i]; - const cg1EdgeMap = {}; // [node1.id-node2.id-edge.label]: count - cg1.edges.forEach(edge => { - const key = `${edge.source}-${edge.target}-${edge.label}`; - if (!cg1EdgeMap[key]) cg1EdgeMap[key] = 1; - else cg1EdgeMap[key]++; - }); - - for (let j = currentLength - 1; j > i; j--) { - const cg2 = candidateGraphs[j]; - const cg2EdgeMap = {}; // [node1.id-node2.id-edge.label]: count - cg2.edges.forEach(edge => { - const key = `${edge.source}-${edge.target}-${edge.label}`; - if (!cg2EdgeMap[key]) cg2EdgeMap[key] = 1; - else cg2EdgeMap[key]++; - }); - - let same = true; - if (Object.keys(cg2EdgeMap).length !== Object.keys(cg1EdgeMap).length) { - same = false; - } else { - Object.keys(cg1EdgeMap).forEach(key => { - if (cg2EdgeMap[key] !== cg1EdgeMap[key]) same = false; - }); - } - if (same) { - candidateGraphs.splice(j, 1); - } - } - currentLength = candidateGraphs.length; - } - - return candidateGraphs; -}; - -export default GADDI; diff --git a/packages/graph/src/i-louvain.ts b/packages/graph/src/i-louvain.ts deleted file mode 100644 index f6f6898..0000000 --- a/packages/graph/src/i-louvain.ts +++ /dev/null @@ -1,28 +0,0 @@ -import louvain from './louvain'; -import type { ClusterData, GraphData } from './types'; - -/** - * 社区发现 i-louvain 算法：模块度 + 惯性模块度（即节点属性相似性） - * @param graphData 图数据 - * @param directed 是否有向图，默认为 false - * @param weightPropertyName 权重的属性字段 - * @param threshold 差值阈值 - * @param propertyKey 属性的字段名 - * @param involvedKeys 参与计算的key集合 - * @param uninvolvedKeys 不参与计算的key集合 - * @param inertialWeight 惯性模块度权重 - */ -const iLouvain = ( - graphData: GraphData, - directed: boolean = false, - weightPropertyName: string = 'weight', - threshold: number = 0.0001, - propertyKey: string = undefined, - involvedKeys: string[] = [], - uninvolvedKeys: string[] = ['id'], - inertialWeight: number = 1, - ): ClusterData => { - return louvain(graphData, directed, weightPropertyName, threshold, true, propertyKey, involvedKeys, uninvolvedKeys, inertialWeight); -} - -export default iLouvain; diff --git a/packages/graph/src/iLouvain.ts b/packages/graph/src/iLouvain.ts new file mode 100644 index 0000000..2ad40b5 --- /dev/null +++ b/packages/graph/src/iLouvain.ts @@ -0,0 +1,24 @@ +import { louvain } from './louvain'; +import type { ClusterData, Graph } from "./types"; + +/** + * 社区发现 i-louvain 算法：模块度 + 惯性模块度（即节点属性相似性） + * @param graphData 图数据 + * @param directed 是否有向图，默认为 false + * @param weightPropertyName 权重的属性字段 + * @param threshold 差值阈值 + * @param involvedKeys 参与计算的key集合 + * @param uninvolvedKeys 不参与计算的key集合 + * @param inertialWeight 惯性模块度权重 + */ +export function iLouvain( + graph: Graph, + directed: boolean = false, + weightPropertyName: string = 'weight', + threshold: number = 0.0001, + involvedKeys: string[] = [], + uninvolvedKeys: string[] = ['id'], + inertialWeight: number = 1, +): ClusterData { + return louvain(graph, directed, weightPropertyName, threshold, true, involvedKeys, uninvolvedKeys, inertialWeight); +} \ No newline at end of file diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index a0b8833..ab5387c 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -1,88 +1,4 @@ -import getAdjMatrix from './adjacent-matrix'; -import breadthFirstSearch from './bfs'; -import connectedComponent from './connected-component'; -import getDegree from './degree'; -import { getInDegree, getOutDegree } from './degree'; -import detectCycle, { detectAllCycles, detectAllDirectedCycle, detectAllUndirectedCycle } from './detect-cycle'; -import depthFirstSearch from './dfs'; -import dijkstra from './dijkstra'; -import { findAllPath, findShortestPath } from './find-path'; -import floydWarshall from './floydWarshall'; -import labelPropagation from './label-propagation'; -import louvain from './louvain'; -import iLouvain from './i-louvain'; -import kCore from './k-core'; -import kMeans from './k-means'; -import cosineSimilarity from './cosine-similarity'; -import nodesCosineSimilarity from './nodes-cosine-similarity'; -import minimumSpanningTree from './mts'; -import pageRank from './pageRank'; -import GADDI from './gaddi'; -import Stack from './structs/stack'; -import { getNeighbors } from './util'; -import { IAlgorithm } from './types'; - -const detectDirectedCycle = detectCycle; - -export { - getAdjMatrix, - breadthFirstSearch, - connectedComponent, - getDegree, - getInDegree, - getOutDegree, - detectCycle, - detectDirectedCycle, - detectAllCycles, - detectAllDirectedCycle, - detectAllUndirectedCycle, - depthFirstSearch, - dijkstra, - findAllPath, - findShortestPath, - floydWarshall, - labelPropagation, - louvain, - iLouvain, - kCore, - kMeans, - cosineSimilarity, - nodesCosineSimilarity, - minimumSpanningTree, - pageRank, - getNeighbors, - Stack, - GADDI, - IAlgorithm -}; - -export default { - getAdjMatrix, - breadthFirstSearch, - connectedComponent, - getDegree, - getInDegree, - getOutDegree, - detectCycle, - detectDirectedCycle, - detectAllCycles, - detectAllDirectedCycle, - detectAllUndirectedCycle, - depthFirstSearch, - dijkstra, - findAllPath, - findShortestPath, - floydWarshall, - labelPropagation, - louvain, - iLouvain, - kCore, - kMeans, - cosineSimilarity, - nodesCosineSimilarity, - minimumSpanningTree, - pageRank, - getNeighbors, - Stack, - GADDI, -}; \ No newline at end of file +export * from "./pageRank"; +export * from "./findPath"; +export * from "./louvain"; +export * from "./iLouvain"; diff --git a/packages/graph/src/k-core.ts b/packages/graph/src/k-core.ts deleted file mode 100644 index e62d4d4..0000000 --- a/packages/graph/src/k-core.ts +++ /dev/null @@ -1,37 +0,0 @@ - -import { clone } from '@antv/util'; -import degree from './degree'; -import { GraphData } from './types'; -/** - * k-core算法 找出符合指定核心度的紧密关联的子图结构 - * @param graphData 图数据 - * @param k 核心度数 - */ -const kCore = ( - graphData: GraphData, - k: number = 1, - ): GraphData => { - const data = clone(graphData); - const { nodes = [] } = data; - let { edges = [] } = data; - while (true) { - // 获取图中节点的度数 - const degrees = degree({ nodes, edges}); - const nodeIds = Object.keys(degrees); - // 按照度数进行排序 - nodeIds.sort((a, b) => degrees[a]?.degree - degrees[b]?.degree); - const minIndexId = nodeIds[0]; - if (!nodes.length || degrees[minIndexId]?.degree >= k) { - break; - } - const originIndex = nodes.findIndex(node => node.id === minIndexId); - // 移除度数小于k的节点 - nodes.splice(originIndex, 1); - // 移除度数小于k的节点相关的边 - edges = edges.filter(edge => !(edge.source === minIndexId || edge.target === minIndexId)); - } - - return { nodes, edges }; -} - -export default kCore; diff --git a/packages/graph/src/k-means.ts b/packages/graph/src/k-means.ts deleted file mode 100644 index be017c4..0000000 --- a/packages/graph/src/k-means.ts +++ /dev/null @@ -1,216 +0,0 @@ -import { isEqual, uniq } from '@antv/util'; -import { getAllProperties } from './utils/node-properties'; -import { oneHot, getDistance } from './utils/data-preprocessing'; -import Vector from './utils/vector'; -import { GraphData, ClusterData, DistanceType } from './types'; - -// 获取质心 -const getCentroid = (distanceType, allPropertiesWeight, index) => { - let centroid = []; - switch (distanceType) { - case DistanceType.EuclideanDistance: - centroid = allPropertiesWeight[index]; - break; - default: - centroid = []; - break; - } - return centroid; -} - -/** - * k-means算法 根据节点之间的距离将节点聚类为K个簇 - * @param data 图数据 - * @param k 质心（聚类中心）个数 - * @param propertyKey 属性的字段名 - * @param involvedKeys 参与计算的key集合 - * @param uninvolvedKeys 不参与计算的key集合 - * @param distanceType 距离类型 默认节点属性的欧式距离 - */ -const kMeans = ( - data: GraphData, - k: number = 3, - propertyKey: string = undefined, - involvedKeys: string[] = [], - uninvolvedKeys: string[] = ['id'], - distanceType: DistanceType = DistanceType.EuclideanDistance, -) : ClusterData => { - const { nodes = [], edges = [] } = data; - - const defaultClusterInfo = { - clusters: [ - { - id: "0", - nodes, - } - ], - clusterEdges: [] - }; - - // 距离类型为欧式距离且没有属性时，直接return - if (distanceType === DistanceType.EuclideanDistance && !nodes.every(node => node.hasOwnProperty(propertyKey))){ - return defaultClusterInfo; - } - - // 所有节点属性集合 - let properties = []; - // 所有节点属性one-hot特征向量集合 - let allPropertiesWeight = []; - if (distanceType === DistanceType.EuclideanDistance) { - properties = getAllProperties(nodes, propertyKey); - allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys); - } - if (!allPropertiesWeight.length) { - return defaultClusterInfo; - } - const allPropertiesWeightUniq = uniq(allPropertiesWeight.map(item => item.join(''))); - // 当输入节点数量或者属性集合的长度小于k时，k调整为其中最小的值 - const finalK = Math.min(k, nodes.length, allPropertiesWeightUniq.length); - - // 记录节点的原始index，与allPropertiesWeight对应 - for (let i = 0; i < nodes.length; i++) { - nodes[i].originIndex = i; - } - // 初始化质心（聚类中心） - const centroids = []; - const centroidIndexList = []; - const clusters = []; - for (let i = 0; i < finalK; i++) { - if (i === 0) { - // 随机选取质心（聚类中心） - const randomIndex = Math.floor(Math.random() * nodes.length); - switch (distanceType) { - case DistanceType.EuclideanDistance: - centroids[i] = allPropertiesWeight[randomIndex]; - break; - default: - centroids[i] = []; - break; - } - centroidIndexList.push(randomIndex); - clusters[i] = [nodes[randomIndex]]; - nodes[randomIndex].clusterId = String(i); - } else { - let maxDistance = -Infinity; - let maxDistanceNodeIndex = 0; - // 选取与已有质心平均距离最远的点做为新的质心 - for (let m = 0; m < nodes.length; m++) { - if (!centroidIndexList.includes(m)) { - let totalDistance = 0; - for (let j = 0; j < centroids.length; j++) { - // 求节点到质心的距离（默认节点属性的欧式距离） - let distance = 0; - switch (distanceType) { - case DistanceType.EuclideanDistance: - distance = getDistance(allPropertiesWeight[nodes[m].originIndex], centroids[j], distanceType); - break; - default: - break; - } - totalDistance += distance; - } - // 节点到各质心的平均距离（默认欧式距离） - const avgDistance = totalDistance / centroids.length; - // 记录到已有质心最远的的距离和节点索引 - if (avgDistance > maxDistance && - !centroids.find(centroid => isEqual(centroid, getCentroid(distanceType, allPropertiesWeight, nodes[m].originIndex)))) { - maxDistance = avgDistance; - maxDistanceNodeIndex = m; - } - } - } - - centroids[i] = getCentroid(distanceType, allPropertiesWeight, maxDistanceNodeIndex); - centroidIndexList.push(maxDistanceNodeIndex); - clusters[i] = [nodes[maxDistanceNodeIndex]]; - nodes[maxDistanceNodeIndex].clusterId = String(i); - } - } - - - let iterations = 0; - while (true) { - for (let i = 0; i < nodes.length; i++) { - let minDistanceIndex = 0; - let minDistance = Infinity; - if (!(iterations === 0 && centroidIndexList.includes(i))) { - for (let j = 0; j < centroids.length; j++) { - // 求节点到质心的距离（默认节点属性的欧式距离） - let distance = 0; - switch (distanceType) { - case DistanceType.EuclideanDistance: - distance = getDistance(allPropertiesWeight[i], centroids[j], distanceType); - break; - default: - break; - } - // 记录节点最近的质心的索引 - if (distance < minDistance) { - minDistance = distance; - minDistanceIndex = j; - } - } - - // 从原来的类别删除节点 - if (nodes[i].clusterId !== undefined) { - for (let n = clusters[Number(nodes[i].clusterId)].length - 1; n >= 0 ; n--) { - if (clusters[Number(nodes[i].clusterId)][n].id === nodes[i].id) { - clusters[Number(nodes[i].clusterId)].splice(n, 1); - } - } - } - // 将节点划分到距离最小的质心（聚类中心）所对应的类中 - nodes[i].clusterId = String(minDistanceIndex); - clusters[minDistanceIndex].push(nodes[i]); - } - } - - // 是否存在质心（聚类中心）移动 - let centroidsEqualAvg = false; - for (let i = 0; i < clusters.length; i ++) { - const clusterNodes = clusters[i]; - let totalVector = new Vector([]); - for (let j = 0; j < clusterNodes.length; j++) { - totalVector = totalVector.add(new Vector(allPropertiesWeight[clusterNodes[j].originIndex])); - } - // 计算每个类别的均值向量 - const avgVector = totalVector.avg(clusterNodes.length); - // 如果均值向量不等于质心向量 - if (!avgVector.equal(new Vector(centroids[i]))) { - centroidsEqualAvg = true; - // 移动/更新每个类别的质心（聚类中心）到该均值向量 - centroids[i] = avgVector.getArr(); - } - } - iterations++; - // 如果每个节点都归属了类别，且不存在质心（聚类中心）移动或者迭代次数超过1000，则停止 - if (nodes.every(node => node.clusterId !== undefined) && centroidsEqualAvg || iterations >= 1000) { - break; - } - } - - // get the cluster edges - const clusterEdges = []; - const clusterEdgeMap = {}; - edges.forEach(edge => { - const { source, target } = edge; - const sourceClusterId = nodes.find(node => node.id === source)?.clusterId; - const targetClusterId = nodes.find(node => node.id === target)?.clusterId; - const newEdgeId = `${sourceClusterId}---${targetClusterId}`; - if (clusterEdgeMap[newEdgeId]) { - clusterEdgeMap[newEdgeId].count++; - } else { - const newEdge = { - source: sourceClusterId, - target: targetClusterId, - count: 1 - }; - clusterEdgeMap[newEdgeId] = newEdge; - clusterEdges.push(newEdge); - } - }); - - return { clusters, clusterEdges }; -} - -export default kMeans; diff --git a/packages/graph/src/label-propagation.ts b/packages/graph/src/label-propagation.ts deleted file mode 100644 index ea5dc3c..0000000 --- a/packages/graph/src/label-propagation.ts +++ /dev/null @@ -1,151 +0,0 @@ - -import getAdjMatrix from './adjacent-matrix' -import { uniqueId } from './util'; -import { GraphData, ClusterData } from './types'; - -/** - * 标签传播算法 - * @param graphData 图数据 - * @param directed 是否有向图，默认为 false - * @param weightPropertyName 权重的属性字段 - * @param maxIteration 最大迭代次数 - */ -const labelPropagation = ( - graphData: GraphData, - directed: boolean = false, - weightPropertyName: string = 'weight', - maxIteration: number = 1000 -): ClusterData => { - // the origin data - const { nodes = [], edges = [] } = graphData; - - const clusters = {}; - const nodeMap = {}; - // init the clusters and nodeMap - nodes.forEach((node, i) => { - const cid: string = uniqueId(); - node.clusterId = cid; - clusters[cid] = { - id: cid, - nodes: [node] - }; - nodeMap[node.id] = { - node, - idx: i - }; - }); - - // the adjacent matrix of calNodes inside clusters - const adjMatrix = getAdjMatrix(graphData, directed); - // the sum of each row in adjacent matrix - const ks = []; - /** - * neighbor nodes (id for key and weight for value) for each node - * neighbors = { - * id(node_id): { id(neighbor_1_id): weight(weight of the edge), id(neighbor_2_id): weight(weight of the edge), ... }, - * ... - * } - */ - const neighbors = {}; - adjMatrix.forEach((row, i) => { - let k = 0; - const iid = nodes[i].id; - neighbors[iid] = {}; - row.forEach((entry, j) => { - if (!entry) return; - k += entry; - const jid = nodes[j].id; - neighbors[iid][jid] = entry; - }); - ks.push(k); - }); - - let iter = 0; - - while (iter < maxIteration) { - let changed = false; - nodes.forEach(node => { - const neighborClusters = {}; - Object.keys(neighbors[node.id]).forEach(neighborId => { - const neighborWeight = neighbors[node.id][neighborId]; - const neighborNode = nodeMap[neighborId].node; - const neighborClusterId = neighborNode.clusterId; - if (!neighborClusters[neighborClusterId]) neighborClusters[neighborClusterId] = 0; - neighborClusters[neighborClusterId] += neighborWeight; - }); - // find the cluster with max weight - let maxWeight = -Infinity; - let bestClusterIds = []; - Object.keys(neighborClusters).forEach(clusterId => { - if (maxWeight < neighborClusters[clusterId]) { - maxWeight = neighborClusters[clusterId]; - bestClusterIds = [clusterId]; - } else if (maxWeight === neighborClusters[clusterId]) { - bestClusterIds.push(clusterId); - } - }); - if (bestClusterIds.length === 1 && bestClusterIds[0] === node.clusterId) return; - const selfClusterIdx = bestClusterIds.indexOf(node.clusterId); - if (selfClusterIdx >= 0) bestClusterIds.splice(selfClusterIdx, 1); - if (bestClusterIds && bestClusterIds.length) { - changed = true; - - // remove from origin cluster - const selfCluster = clusters[node.clusterId as string]; - const nodeInSelfClusterIdx = selfCluster.nodes.indexOf(node); - selfCluster.nodes.splice(nodeInSelfClusterIdx, 1); - - // move the node to the best cluster - const randomIdx = Math.floor(Math.random() * bestClusterIds.length); - const bestCluster = clusters[bestClusterIds[randomIdx]]; - bestCluster.nodes.push(node); - node.clusterId = bestCluster.id; - } - }); - if (!changed) break; - iter++; - } - - // delete the empty clusters - Object.keys(clusters).forEach(clusterId => { - const cluster = clusters[clusterId]; - if (!cluster.nodes || !cluster.nodes.length) { - delete clusters[clusterId]; - } - }); - - // get the cluster edges - const clusterEdges = []; - const clusterEdgeMap = {}; - edges.forEach(edge => { - const { source, target } = edge; - const weight = edge[weightPropertyName] || 1; - const sourceClusterId = nodeMap[source].node.clusterId; - const targetClusterId = nodeMap[target].node.clusterId; - const newEdgeId = `${sourceClusterId}---${targetClusterId}`; - if (clusterEdgeMap[newEdgeId]) { - clusterEdgeMap[newEdgeId].weight += weight; - clusterEdgeMap[newEdgeId].count++; - } else { - const newEdge = { - source: sourceClusterId, - target: targetClusterId, - weight, - count: 1 - }; - clusterEdgeMap[newEdgeId] = newEdge; - clusterEdges.push(newEdge); - } - }); - - const clustersArray = []; - Object.keys(clusters).forEach(clusterId => { - clustersArray.push(clusters[clusterId]); - }); - return { - clusters: clustersArray, - clusterEdges - } -} - -export default labelPropagation; diff --git a/packages/graph/src/louvain.ts b/packages/graph/src/louvain.ts index 75abc49..f37ba6d 100644 --- a/packages/graph/src/louvain.ts +++ b/packages/graph/src/louvain.ts @@ -1,26 +1,30 @@ -import { clone } from '@antv/util'; -import getAdjMatrix from './adjacent-matrix'; -import { NodeConfig, ClusterData, GraphData, ClusterMap } from './types'; -import Vector from './utils/vector'; -import { getAllProperties } from './utils/node-properties'; -import { oneHot } from './utils/data-preprocessing'; +import { ID, Node } from "@antv/graphlib"; +import { clone } from "@antv/util"; +import { Cluster, ClusterData, ClusterMap, Graph, NodeData } from "./types"; +import { getAllProperties, oneHot } from "./utils"; +import { graph2AdjacencyMatrix } from "./adjMatrix"; +import { Vector } from "./vector"; -const getModularity = ( - nodes: NodeConfig[], +/** + * The quality of the communities referred as partitions hereafter is measured by Modularity of the partition. + * @see https://medium.com/walmartglobaltech/demystifying-louvains-algorithm-and-its-implementation-in-gpu-9a07cdd3b010 + */ +function getModularity( + nodes: Node[], adjMatrix: number[][], ks: number[], m: number -) => { +) { const length = adjMatrix.length; - const param = 2 * m; + const param = 2 * m; // number if links let modularity = 0; for (let i = 0; i < length; i++) { - const clusteri = nodes[i].clusterId; + const clusteri = nodes[i].data.clusterId as string; for (let j = 0; j < length; j++) { - const clusterj = nodes[j].clusterId; - if (clusteri !== clusterj) continue; - const entry = adjMatrix[i][j] || 0; - const ki = ks[i] || 0; + const clusterj = nodes[j].data.clusterId as string; + if (clusteri !== clusterj) continue; // 1 if x = y and 0 otherwise + const entry = adjMatrix[i][j] || 0; // Aij: the weightof the edge between i & j + const ki = ks[i] || 0; // Ki: degree of the node const kj = ks[j] || 0; modularity += (entry - ki * kj / param); } @@ -30,10 +34,10 @@ const getModularity = ( } // 模块惯性度，衡量属性相似度 -const getInertialModularity = ( - nodes: NodeConfig[] = [], +function getInertialModularity( + nodes: Node[] = [], allPropertiesWeight: number[][], -) => { +) { const length = nodes.length; let totalProperties = new Vector([]); for (let i = 0; i < length; i++) { @@ -52,13 +56,13 @@ const getInertialModularity = ( } // 任意两点间的欧式平方距离 - let squareEuclideanDistanceInfo = []; + const squareEuclideanDistanceInfo: number[][] = []; nodes.forEach(() => { squareEuclideanDistanceInfo.push([]); }); for (let i = 0; i < length; i++) { const propertiesi = new Vector(allPropertiesWeight[i]); - nodes[i]['clusterInertial'] = 0; + nodes[i].data['clusterInertial'] = 0; for (let j = 0; j < length; j++) { if ( i === j) { squareEuclideanDistanceInfo[i][j] = 0; @@ -66,26 +70,26 @@ const getInertialModularity = ( } const propertiesj = new Vector(allPropertiesWeight[j]); squareEuclideanDistanceInfo[i][j] = propertiesi.squareEuclideanDistance(propertiesj); - nodes[i]['clusterInertial'] += squareEuclideanDistanceInfo[i][j]; + (nodes[i].data['clusterInertial'] as number) += squareEuclideanDistanceInfo[i][j]; } } // 计算模块惯性度 - let inertialModularity: number = 0; + let inertialModularity = 0; const param = 2 * length * variance; for (let i = 0; i < length; i++) { - const clusteri = nodes[i].clusterId; + const clusteri = nodes[i].data.clusterId; for (let j = 0; j < length; j++) { - const clusterj = nodes[j].clusterId; + const clusterj = nodes[j].data.clusterId; if ( i === j || clusteri !== clusterj) continue; - const inertial = (nodes[i].clusterInertial * nodes[j].clusterInertial) / Math.pow(param, 2) - squareEuclideanDistanceInfo[i][j] / param; + const inertial = ((nodes[i].data.clusterInertial as number) * (nodes[j].data.clusterInertial as number)) + / Math.pow(param, 2) - squareEuclideanDistanceInfo[i][j] / param; inertialModularity += inertial; } } return Number(inertialModularity.toFixed(4)); } - /** * 社区发现 louvain 算法 * @param graphData 图数据 @@ -98,48 +102,49 @@ const getInertialModularity = ( * @param uninvolvedKeys 不参与计算的key集合 * @param inertialWeight 惯性模块度权重 */ -const louvain = ( - graphData: GraphData, +export function louvain( + graph: Graph, directed: boolean = false, weightPropertyName: string = 'weight', threshold: number = 0.0001, inertialModularity: boolean = false, - propertyKey: string = undefined, involvedKeys: string[] = [], uninvolvedKeys: string[] = ['id'], inertialWeight: number = 1, -): ClusterData => { - // the origin data - const { nodes = [], edges = [] } = graphData; +): ClusterData { + const nodes = graph.getAllNodes(); + const edges = graph.getAllEdges(); - let allPropertiesWeight = []; + let allPropertiesWeight: number[][] = []; if (inertialModularity) { nodes.forEach((node, index) => { - node.properties = node.properties || {}; - node.originIndex = index; - }) + node.data.originIndex = index; + }); - let nodeTypeInfo = []; - if (nodes.every(node => node.hasOwnProperty('nodeType'))) { - nodeTypeInfo = Array.from(new Set(nodes.map(node => node.nodeType))); - nodes.forEach(node => { - node.properties.nodeType = nodeTypeInfo.findIndex(nodeType => nodeType === node.nodeType); - }) + let nodeTypeInfo: string[] = []; + if (nodes.every((node) => 'nodeType' in node.data)) { + nodeTypeInfo = Array.from(new Set(nodes.map((node) => node.data.nodeType as string))); + nodes.forEach((node) => { + node.data.nodeType = nodeTypeInfo.findIndex((nodeType) => nodeType === node.data.nodeType); + }); } // 所有节点属性集合 - const properties = getAllProperties(nodes, propertyKey); + const properties = getAllProperties(nodes); + // 所有节点属性one-hot特征向量集合 - allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys); + allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys) as number[][]; } + /** + * 1. To start with each node is assigned to a different community or partition. + * The number of partitions is equal to number of nodes N. + */ let uniqueId = 1; - const clusters: ClusterMap = {}; - const nodeMap = {}; - // init the clusters and nodeMap + const nodeMap: Record; idx: number }> = {}; nodes.forEach((node, i) => { const cid: string = String(uniqueId++); - node.clusterId = cid; + node.data.clusterId = cid; clusters[cid] = { id: cid, nodes: [node] @@ -150,9 +155,9 @@ const louvain = ( }; }); // the adjacent matrix of calNodes inside clusters - const adjMatrix = getAdjMatrix(graphData, directed); + const adjMatrix = graph2AdjacencyMatrix(graph, directed); // the sum of each row in adjacent matrix - const ks = []; + const ks: number[] = []; /** * neighbor nodes (id for key and weight for value) for each node * neighbors = { @@ -160,7 +165,7 @@ const louvain = ( * ... * } */ - const neighbors = {}; + const neighbors: Record> = {}; // the sum of the weights of all edges in the graph let m = 0; adjMatrix.forEach((row, i) => { @@ -183,10 +188,10 @@ const louvain = ( let previousModularity = Infinity; let iter = 0; - let finalNodes = []; - let finalClusters = {}; + let finalNodes: Node[] = []; + let finalClusters: ClusterMap = {}; while (true) { - if (inertialModularity && nodes.every(node => node.hasOwnProperty('properties'))) { + if (inertialModularity && nodes.every((node) => node.hasOwnProperty('properties'))) { totalModularity = getModularity(nodes, adjMatrix, ks, m) + getInertialModularity(nodes, allPropertiesWeight) * inertialWeight; } else { totalModularity = getModularity(nodes, adjMatrix, ks, m); @@ -202,30 +207,26 @@ const louvain = ( const increaseWithinThreshold = totalModularity > 0 && totalModularity > previousModularity && totalModularity - previousModularity < threshold; // 总模块度增加才更新最优解 if (totalModularity > previousModularity) { - finalNodes = nodes.map(node => ({ - node, - clusterId: node.clusterId - })); finalClusters = clone(clusters); previousModularity = totalModularity; } // whether to terminate the iterations - if ( increaseWithinThreshold || iter > 100) { + if (increaseWithinThreshold || iter > 100) { break; - }; + } iter++; // pre compute some values for current clusters - Object.keys(clusters).forEach(clusterId => { + Object.keys(clusters).forEach((clusterId) => { // sum of weights of edges to nodes in cluster let sumTot = 0; - edges.forEach(edge => { + edges.forEach((edge) => { const { source, target } = edge; - const sourceClusterId = nodeMap[source].node.clusterId; - const targetClusterId = nodeMap[target].node.clusterId; + const sourceClusterId = nodeMap[source].node.data.clusterId; + const targetClusterId = nodeMap[target].node.data.clusterId; if ((sourceClusterId === clusterId && targetClusterId !== clusterId) || (targetClusterId === clusterId && sourceClusterId !== clusterId)) { - sumTot = sumTot + (edge[weightPropertyName] as number || 1); + sumTot = sumTot + (edge.data[weightPropertyName] as number || 1); } }); clusters[clusterId].sumTot = sumTot; @@ -234,38 +235,38 @@ const louvain = ( // move the nodes to increase the delta modularity nodes.forEach((node, i) => { - const selfCluster = clusters[node.clusterId as string]; + const selfCluster = clusters[node.data.clusterId]; let bestIncrease = 0; - let bestCluster; + let bestCluster: Cluster; const commonParam = ks[i] / (2 * m); // sum of weights of edges from node to nodes in cluster let kiin = 0; const selfClusterNodes = selfCluster.nodes; - selfClusterNodes.forEach(scNode => { + selfClusterNodes.forEach((scNode) => { const scNodeIdx = nodeMap[scNode.id].idx; kiin += adjMatrix[i][scNodeIdx] || 0; }); // the modurarity for **removing** the node i from the origin cluster of node i const removeModurarity = kiin - selfCluster.sumTot * commonParam; // nodes for **removing** node i into this neighbor cluster - const selfClusterNodesAfterRemove = selfClusterNodes.filter(scNode => scNode.id !== node.id); - let propertiesWeightRemove = []; + const selfClusterNodesAfterRemove = selfClusterNodes.filter((scNode) => scNode.id !== node.id); + const propertiesWeightRemove: number[][] = []; selfClusterNodesAfterRemove.forEach((nodeRemove, index) => { - propertiesWeightRemove[index] = allPropertiesWeight[nodeRemove.originIndex]; - }) + propertiesWeightRemove[index] = allPropertiesWeight[nodeRemove.data.originIndex as number]; + }); // the inertialModularity for **removing** the node i from the origin cluster of node i - const removeInertialModularity = getInertialModularity(selfClusterNodesAfterRemove, allPropertiesWeight) * inertialWeight; + const removeInertialModularity = inertialModularity ? getInertialModularity(selfClusterNodesAfterRemove, allPropertiesWeight) * inertialWeight : 0; // the neightbors of the node const nodeNeighborIds = neighbors[node.id]; - Object.keys(nodeNeighborIds).forEach(neighborNodeId => { + Object.keys(nodeNeighborIds).forEach((neighborNodeId) => { const neighborNode = nodeMap[neighborNodeId].node; - const neighborClusterId = neighborNode.clusterId; + const neighborClusterId = neighborNode.data.clusterId; // if the node and the neighbor of node are in the same cluster, reutrn - if (neighborClusterId === node.clusterId) return; + if (neighborClusterId === node.data.clusterId) return; const neighborCluster = clusters[neighborClusterId]; const clusterNodes = neighborCluster.nodes; @@ -274,7 +275,7 @@ const louvain = ( // sum of weights of edges from node to nodes in cluster let neighborClusterKiin = 0; - clusterNodes.forEach(cNode => { + clusterNodes.forEach((cNode) => { const cNodeIdx = nodeMap[cNode.id].idx; neighborClusterKiin += adjMatrix[i][cNodeIdx] || 0; }); @@ -283,12 +284,12 @@ const louvain = ( const addModurarity = neighborClusterKiin - neighborCluster.sumTot * commonParam; // nodes for **adding** node i into this neighbor cluster const clusterNodesAfterAdd= clusterNodes.concat([node]); - let propertiesWeightAdd = []; + const propertiesWeightAdd: number[][] = []; clusterNodesAfterAdd.forEach((nodeAdd, index) => { - propertiesWeightAdd[index] = allPropertiesWeight[nodeAdd.originIndex]; - }) + propertiesWeightAdd[index] = allPropertiesWeight[nodeAdd.data.originIndex as number]; + }); // the inertialModularity for **adding** node i into this neighbor cluster - const addInertialModularity = getInertialModularity(clusterNodesAfterAdd, allPropertiesWeight) * inertialWeight; + const addInertialModularity = inertialModularity ? getInertialModularity(clusterNodesAfterAdd, allPropertiesWeight) * inertialWeight : 0; // the increase modurarity is the difference between addModurarity and removeModurarity let increase = addModurarity - removeModurarity; @@ -306,8 +307,8 @@ const louvain = ( // if found a best cluster to move into if (bestIncrease > 0) { bestCluster.nodes.push(node); - const previousClusterId = node.clusterId; - node.clusterId = bestCluster.id; + const previousClusterId = node.data.clusterId; + node.data.clusterId = bestCluster.id; // move the node to the best cluster const nodeInSelfClusterIdx = selfCluster.nodes.indexOf(node); // remove from origin cluster @@ -316,17 +317,17 @@ const louvain = ( // sum of weights of edges to nodes in cluster let neighborClusterSumTot = 0; let selfClusterSumTot = 0; - edges.forEach(edge => { + edges.forEach((edge) => { const { source, target } = edge; - const sourceClusterId = nodeMap[source].node.clusterId; - const targetClusterId = nodeMap[target].node.clusterId; + const sourceClusterId = nodeMap[source].node.data.clusterId; + const targetClusterId = nodeMap[target].node.data.clusterId; if ((sourceClusterId === bestCluster.id && targetClusterId !== bestCluster.id) || (targetClusterId === bestCluster.id && sourceClusterId !== bestCluster.id)) { - neighborClusterSumTot = neighborClusterSumTot + (edge[weightPropertyName] as number || 1); + neighborClusterSumTot = neighborClusterSumTot + (edge.data[weightPropertyName] as number || 1); } if ((sourceClusterId === previousClusterId && targetClusterId !== previousClusterId) || (targetClusterId === previousClusterId && sourceClusterId !== previousClusterId)) { - selfClusterSumTot = selfClusterSumTot + (edge[weightPropertyName] as number || 1); + selfClusterSumTot = selfClusterSumTot + (edge.data[weightPropertyName] as number || 1); } }); @@ -338,7 +339,7 @@ const louvain = ( } // delete the empty clusters, assign increasing clusterId - const newClusterIdMap = {} + const newClusterIdMap: Record = {}; let clusterIdx = 0; Object.keys(finalClusters).forEach((clusterId) => { const cluster = finalClusters[clusterId]; @@ -351,51 +352,69 @@ const louvain = ( return; } cluster.id = newId; - cluster.nodes = cluster.nodes.map(item => ({ id: item.id, clusterId: newId })); + cluster.nodes.forEach((node) => { + node.data.clusterId = newId; + }); finalClusters[newId] = cluster; newClusterIdMap[clusterId] = newId; delete finalClusters[clusterId]; clusterIdx ++; }); // restore node clusterId - finalNodes.forEach(nodeInfo => { - const { node, clusterId } = nodeInfo; - if (!node) return; - node.clusterId = clusterId; - if (node.clusterId && newClusterIdMap[node.clusterId]) node.clusterId = newClusterIdMap[node.clusterId] - }) + finalNodes.forEach((node) => { + if (node.data.clusterId && newClusterIdMap[node.data.clusterId]) { + node.data.clusterId = newClusterIdMap[node.data.clusterId]; + } + }); // get the cluster edges - const clusterEdges = []; - const clusterEdgeMap = {}; - edges.forEach(edge => { + const clusterEdges: { + id: ID; + source: string; + target: string; + data: { + weight: number; + count: number; + } + }[] = []; + const clusterEdgeMap: Record = {}; + edges.forEach((edge) => { const { source, target } = edge; - const weight = edge[weightPropertyName] || 1; - const sourceClusterId = nodeMap[source].node.clusterId; - const targetClusterId = nodeMap[target].node.clusterId; + const weight = edge.data[weightPropertyName] as number || 1; + const sourceClusterId = nodeMap[source].node.data.clusterId; + const targetClusterId = nodeMap[target].node.data.clusterId; if (!sourceClusterId || !targetClusterId) return; const newEdgeId = `${sourceClusterId}---${targetClusterId}`; if (clusterEdgeMap[newEdgeId]) { - clusterEdgeMap[newEdgeId].weight += weight; - clusterEdgeMap[newEdgeId].count++; + clusterEdgeMap[newEdgeId].data.weight += weight; + clusterEdgeMap[newEdgeId].data.count++; } else { const newEdge = { + id: edge.id, source: sourceClusterId, target: targetClusterId, - weight, - count: 1 + data: { + weight, + count: 1 + } }; clusterEdgeMap[newEdgeId] = newEdge; clusterEdges.push(newEdge); } }); - const clustersArray = []; - Object.keys(finalClusters).forEach(clusterId => { + const clustersArray: Cluster[] = []; + Object.keys(finalClusters).forEach((clusterId) => { clustersArray.push(finalClusters[clusterId]); }); return { clusters: clustersArray, clusterEdges - } + }; } - -export default louvain; diff --git a/packages/graph/src/mts.ts b/packages/graph/src/mts.ts deleted file mode 100644 index e97edad..0000000 --- a/packages/graph/src/mts.ts +++ /dev/null @@ -1,116 +0,0 @@ -import UnionFind from './structs/union-find'; -import MinBinaryHeap from './structs/binary-heap'; -import { GraphData, EdgeConfig } from './types'; -import { getEdgesByNodeId } from './util'; - -/** - * Prim algorithm，use priority queue，复杂度 O(E+V*logV), V: 节点数量，E: 边的数量 - * refer: https://en.wikipedia.org/wiki/Prim%27s_algorithm - * @param graph - * @param weight 指定用于作为边权重的属性，若不指定，则认为所有边权重一致 - */ -const primMST = (graphData: GraphData, weight?: string) => { - const selectedEdges = []; - const { nodes = [], edges = [] } = graphData; - if (nodes.length === 0) { - return selectedEdges; - } - - // 从nodes[0]开始 - const currNode = nodes[0]; - const visited = new Set(); - visited.add(currNode); - - // 用二叉堆维护距已加入节点的其他节点的边的权值 - const compareWeight = (a: EdgeConfig, b: EdgeConfig) => { - if (weight) { - return a.weight - b.weight; - } - return 0; - - }; - const edgeQueue = new MinBinaryHeap(compareWeight); - getEdgesByNodeId(currNode.id, edges).forEach((edge) => { - edgeQueue.insert(edge); - }); - - while (!edgeQueue.isEmpty()) { - // 选取与已加入的结点之间边权最小的结点 - const currEdge: EdgeConfig = edgeQueue.delMin(); - const source = currEdge.source; - const target = currEdge.target; - if (visited.has(source) && visited.has(target)) continue; - selectedEdges.push(currEdge); - - if (!visited.has(source)) { - visited.add(source); - getEdgesByNodeId(source, edges).forEach((edge) => { - edgeQueue.insert(edge); - }); - } - if (!visited.has(target)) { - visited.add(target); - getEdgesByNodeId(target, edges).forEach((edge) => { - edgeQueue.insert(edge); - }); - } - } - return selectedEdges; -}; - -/** - * Kruskal algorithm，复杂度 O(E*logE), E: 边的数量 - * refer: https://en.wikipedia.org/wiki/Kruskal%27s_algorithm - * @param graph - * @param weight 指定用于作为边权重的属性，若不指定，则认为所有边权重一致 - * @return IEdge[] 返回构成MST的边的数组 - */ -const kruskalMST = (graphData: GraphData, weight?: string): EdgeConfig[] => { - const selectedEdges = []; - const { nodes = [], edges = [] } = graphData - if (nodes.length === 0) { - return selectedEdges; - } - - // 若指定weight，则将所有的边按权值从小到大排序 - const weightEdges = edges.map((edge) => edge); - if (weight) { - weightEdges.sort((a, b) => { - return a.weight - b.weight; - }); - } - const disjointSet = new UnionFind(nodes.map((n) => n.id)); - - // 从权值最小的边开始，如果这条边连接的两个节点于图G中不在同一个连通分量中，则添加这条边 - // 直到遍历完所有点或边 - while (weightEdges.length > 0) { - const curEdge = weightEdges.shift(); - const source = curEdge.source; - const target = curEdge.target; - if (!disjointSet.connected(source, target)) { - selectedEdges.push(curEdge); - disjointSet.union(source, target); - } - } - return selectedEdges; -}; - -/** - * 最小生成树 - * refer: https://en.wikipedia.org/wiki/Kruskal%27s_algorithm - * @param graph - * @param weight 指定用于作为边权重的属性，若不指定，则认为所有边权重一致 - * @param algo 'prim' | 'kruskal' 算法类型 - * @return EdgeConfig[] 返回构成MST的边的数组 - */ -const minimumSpanningTree = (graphData: GraphData, weight?: string, algo?: string): EdgeConfig[] => { - const algos = { - prim: primMST, - kruskal: kruskalMST, - }; - if (!algo) return kruskalMST(graphData, weight); - - return algos[algo](graphData, weight); -} - -export default minimumSpanningTree diff --git a/packages/graph/src/nodes-cosine-similarity.ts b/packages/graph/src/nodes-cosine-similarity.ts deleted file mode 100644 index 2e593eb..0000000 --- a/packages/graph/src/nodes-cosine-similarity.ts +++ /dev/null @@ -1,50 +0,0 @@ -import { clone } from '@antv/util'; -import { NodeConfig } from './types'; -import { getAllProperties } from './utils/node-properties'; -import { oneHot } from './utils/data-preprocessing'; -import cosineSimilarity from './cosine-similarity'; -/** - * nodes-cosine-similarity算法 基于节点属性计算余弦相似度(基于种子节点寻找相似节点) - * @param nodes 图节点数据 - * @param seedNode 种子节点 - * @param propertyKey 属性的字段名 - * @param involvedKeys 参与计算的key集合 - * @param uninvolvedKeys 不参与计算的key集合 - */ -const nodesCosineSimilarity = ( - nodes: NodeConfig[] = [], - seedNode: NodeConfig, - propertyKey: string = undefined, - involvedKeys: string[] = [], - uninvolvedKeys: string[] = [], -): { - allCosineSimilarity: number[], - similarNodes: NodeConfig[], -} => { - const similarNodes = clone(nodes.filter(node => node.id !== seedNode.id)); - const seedNodeIndex = nodes.findIndex(node => node.id === seedNode.id); - // 所有节点属性集合 - const properties = getAllProperties(nodes, propertyKey); - // 所有节点属性one-hot特征向量集合 - const allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys); - // 种子节点属性 - const seedNodeProperties = allPropertiesWeight[seedNodeIndex]; - - const allCosineSimilarity: number[] = []; - similarNodes.forEach((node, index) => { - if (node.id !== seedNode.id) { - // 节点属性 - const nodeProperties = allPropertiesWeight[index]; - // 计算节点向量和种子节点向量的余弦相似度 - const cosineSimilarityValue = cosineSimilarity(nodeProperties, seedNodeProperties); - allCosineSimilarity.push(cosineSimilarityValue); - node.cosineSimilarity = cosineSimilarityValue; - } - }); - - // 将返回的节点按照余弦相似度大小排序 - similarNodes.sort((a, b) => b.cosineSimilarity - a.cosineSimilarity); - return { allCosineSimilarity, similarNodes }; -} - -export default nodesCosineSimilarity; diff --git a/packages/graph/src/pageRank.ts b/packages/graph/src/pageRank.ts index bda8cfd..f2e63db 100644 --- a/packages/graph/src/pageRank.ts +++ b/packages/graph/src/pageRank.ts @@ -1,55 +1,61 @@ -import { GraphData } from "./types"; -import degree from './degree' -import { getNeighbors } from "./util"; +import { ID } from "@antv/graphlib"; +import { Graph } from "./types"; + +interface Params { + alpha: number; + maxIterations: number; + tolerance: number; +} /** * PageRank https://en.wikipedia.org/wiki/PageRank * refer: https://github.com/anvaka/ngraph.pagerank - * @param graph - * @param epsilon 判断是否收敛的精度值，默认 0.000001 - * @param linkProb 阻尼系数（dumping factor），指任意时刻，用户访问到某节点后继续访问该节点链接的下一个节点的概率，经验值 0.85 + * @param graph + * @param params */ -const pageRank = (graphData: GraphData, epsilon?: number, linkProb?: number): { - [key: string]: number; -} => { - if (typeof epsilon !== 'number') epsilon = 0.000001; - if (typeof linkProb !== 'number') linkProb = 0.85; +export const pageRank = ( + graph: Graph, + params?: Params, +): Record => { + const { + tolerance = 1e-5, + alpha = 0.85, + maxIterations = 1000 + } = params || {}; let distance = 1; let leakedRank = 0; - let maxIterations = 1000; - const { nodes = [], edges = [] } = graphData; + const nodes = graph.getAllNodes(); const nodesCount = nodes.length; - let currentRank; - const curRanks = {}; - const prevRanks = {} + let currentRank: number; + const curRanks: Record = {}; + const prevRanks: Record = {}; - // Initialize pageranks 初始化 for (let j = 0; j < nodesCount; ++j) { const node = nodes[j]; const nodeId = node.id; - curRanks[nodeId] = (1 / nodesCount) - prevRanks[nodeId] = (1 / nodesCount) + curRanks[nodeId] = 1 / nodesCount; + prevRanks[nodeId] = 1 / nodesCount; } - const nodeDegree = degree(graphData) - while (maxIterations > 0 && distance > epsilon) { + let iterations = maxIterations; + while (iterations > 0 && distance > tolerance) { leakedRank = 0; for (let j = 0; j < nodesCount; ++j) { const node = nodes[j]; const nodeId = node.id; currentRank = 0; - if (nodeDegree[node.id].inDegree === 0) { + if (graph.getDegree(nodeId, 'in') === 0) { curRanks[nodeId] = 0; } else { - const neighbors = getNeighbors(nodeId, edges, 'source'); + const neighbors = graph.getRelatedEdges(nodeId, "in"); for (let i = 0; i < neighbors.length; ++i) { const neighbor = neighbors[i]; - const outDegree: number = nodeDegree[neighbor].outDegree; - if (outDegree > 0) currentRank += (prevRanks[neighbor] / outDegree); + const outDegree: number = graph.getDegree(neighbor.source, 'out'); + if (outDegree > 0) currentRank += prevRanks[neighbor.source] / outDegree; } - curRanks[nodeId] = linkProb * currentRank; + curRanks[nodeId] = alpha * currentRank; leakedRank += curRanks[nodeId]; } } @@ -63,10 +69,8 @@ const pageRank = (graphData: GraphData, epsilon?: number, linkProb?: number): { distance += Math.abs(currentRank - prevRanks[nodeId]); prevRanks[nodeId] = currentRank; } - maxIterations -= 1 + iterations -= 1; } return prevRanks; -} - -export default pageRank +}; diff --git a/packages/graph/src/structs/binary-heap.ts b/packages/graph/src/structs/binary-heap.ts deleted file mode 100644 index bf3eb70..0000000 --- a/packages/graph/src/structs/binary-heap.ts +++ /dev/null @@ -1,90 +0,0 @@ -const defaultCompare = (a, b) => { - return a - b; -}; - -export default class MinBinaryHeap { - list: any[]; - - compareFn: (a: any, b: any) => number; - - constructor(compareFn = defaultCompare) { - this.compareFn = compareFn; - this.list = []; - } - - getLeft(index) { - return 2 * index + 1; - } - - getRight(index) { - return 2 * index + 2; - } - - getParent(index) { - if (index === 0) { - return null; - } - return Math.floor((index - 1) / 2); - } - - isEmpty() { - return this.list.length <= 0; - } - - top() { - return this.isEmpty() ? undefined : this.list[0]; - } - - delMin() { - const top = this.top(); - const bottom = this.list.pop(); - if (this.list.length > 0) { - this.list[0] = bottom; - this.moveDown(0); - } - return top; - } - - insert(value) { - if (value !== null) { - this.list.push(value); - const index = this.list.length - 1; - this.moveUp(index); - return true; - } - return false; - } - - moveUp(index) { - let parent = this.getParent(index); - while (index && index > 0 && this.compareFn(this.list[parent], this.list[index]) > 0) { - // swap - const tmp = this.list[parent]; - this.list[parent] = this.list[index]; - this.list[index] = tmp; - // [this.list[index], this.list[parent]] = [this.list[parent], this.list[index]] - index = parent; - parent = this.getParent(index); - } - } - - moveDown(index) { - let element = index; - const left = this.getLeft(index); - const right = this.getRight(index); - const size = this.list.length; - if (left !== null && left < size && this.compareFn(this.list[element], this.list[left]) > 0) { - element = left; - } else if ( - right !== null && - right < size && - this.compareFn(this.list[element], this.list[right]) > 0 - ) { - element = right; - } - if (index !== element) { - [this.list[index], this.list[element]] = [this.list[element], this.list[index]]; - this.moveDown(element); - } - } -} diff --git a/packages/graph/src/structs/linked-list.ts b/packages/graph/src/structs/linked-list.ts deleted file mode 100644 index b4ebee6..0000000 --- a/packages/graph/src/structs/linked-list.ts +++ /dev/null @@ -1,245 +0,0 @@ -const defaultComparator = (a, b) => { - if (a === b) { - return true; - } - - return false; -} - -/** - * 链表中单个元素节点 - */ -export class LinkedListNode { - public value; - - public next: LinkedListNode; - - constructor(value, next: LinkedListNode = null) { - this.value = value; - this.next = next; - } - - toString(callback?: any) { - return callback ? callback(this.value) : `${this.value}`; - } -} - -export default class LinkedList { - public head: LinkedListNode; - - public tail: LinkedListNode; - - public compare: Function; - - constructor(comparator = defaultComparator) { - this.head = null; - this.tail = null; - this.compare = comparator; - } - - /** - * 将指定元素添加到链表头部 - * @param value - */ - prepend(value) { - // 在头部添加一个节点 - const newNode = new LinkedListNode(value, this.head); - this.head = newNode; - - if (!this.tail) { - this.tail = newNode; - } - - return this; - } - - /** - * 将指定元素添加到链表中 - * @param value - */ - append(value) { - const newNode = new LinkedListNode(value); - - // 如果不存在头节点，则将创建的新节点作为头节点 - if (!this.head) { - this.head = newNode; - this.tail = newNode; - - return this; - } - - // 将新节点附加到链表末尾 - this.tail.next = newNode; - this.tail = newNode; - - return this; - } - - /** - * 删除指定元素 - * @param value 要删除的元素 - */ - delete(value): LinkedListNode { - if (!this.head) { - return null; - } - - let deleteNode = null; - - // 如果删除的是头部元素，则将next作为头元素 - while (this.head && this.compare(this.head.value, value)) { - deleteNode = this.head; - this.head = this.head.next; - } - - let currentNode = this.head; - - if (currentNode !== null) { - // 如果删除了节点以后，将next节点前移 - while (currentNode.next) { - if (this.compare(currentNode.next.value, value)) { - deleteNode = currentNode.next; - currentNode.next = currentNode.next.next; - } else { - currentNode = currentNode.next; - } - } - } - - // 检查尾部节点是否被删除 - if (this.compare(this.tail.value, value)) { - this.tail = currentNode; - } - - return deleteNode; - } - - /** - * 查找指定的元素 - * @param param0 - */ - find({ value = undefined, callback = undefined }): LinkedListNode { - if (!this.head) { - return null; - } - - let currentNode = this.head; - - while (currentNode) { - // 如果指定了 callback，则按指定的 callback 查找 - if (callback && callback(currentNode.value)) { - return currentNode; - } - - // 如果指定了 value，则按 value 查找 - if (value !== undefined && this.compare(currentNode.value, value)) { - return currentNode; - } - - currentNode = currentNode.next; - } - - return null; - } - - /** - * 删除尾部节点 - */ - deleteTail() { - const deletedTail = this.tail; - - if (this.head === this.tail) { - // 链表中只有一个元素 - this.head = null; - this.tail = null; - return deletedTail; - } - - let currentNode = this.head; - while (currentNode.next) { - if (!currentNode.next.next) { - currentNode.next = null; - } else { - currentNode = currentNode.next; - } - } - - this.tail = currentNode; - - return deletedTail; - } - - /** - * 删除头部节点 - */ - deleteHead() { - if (!this.head) { - return null; - } - - const deletedHead = this.head; - - if (this.head.next) { - this.head = this.head.next; - } else { - this.head = null; - this.tail = null; - } - - return deletedHead; - } - - /** - * 将一组元素转成链表中的节点 - * @param values 链表中的元素 - */ - fromArray(values) { - values.forEach((value) => this.append(value)); - return this; - } - - /** - * 将链表中的节点转成数组元素 - */ - toArray() { - const nodes = []; - - let currentNode = this.head; - - while (currentNode) { - nodes.push(currentNode); - currentNode = currentNode.next; - } - - return nodes; - } - - /** - * 反转链表中的元素节点 - */ - reverse() { - let currentNode = this.head; - let prevNode = null; - let nextNode = null; - while (currentNode) { - // 存储下一个元素节点 - nextNode = currentNode.next; - - // 更改当前节点的下一个节点，以便将它连接到上一个节点上 - currentNode.next = prevNode; - - // 将 prevNode 和 currentNode 向前移动一步 - prevNode = currentNode; - currentNode = nextNode; - } - - this.tail = this.head; - this.head = prevNode; - } - - toString(callback = undefined) { - return this.toArray() - .map((node) => node.toString(callback)) - .toString(); - } -} diff --git a/packages/graph/src/structs/queue.ts b/packages/graph/src/structs/queue.ts deleted file mode 100644 index f265e0c..0000000 --- a/packages/graph/src/structs/queue.ts +++ /dev/null @@ -1,46 +0,0 @@ -import LinkedList from './linked-list'; - -export default class Queue { - public linkedList: LinkedList; - - constructor() { - this.linkedList = new LinkedList(); - } - - /** - * 队列是否为空 - */ - public isEmpty() { - return !this.linkedList.head; - } - - /** - * 读取队列头部的元素， 不删除队列中的元素 - */ - public peek() { - if (!this.linkedList.head) { - return null; - } - return this.linkedList.head.value; - } - - /** - * 在队列的尾部新增一个元素 - * @param value - */ - public enqueue(value) { - this.linkedList.append(value); - } - - /** - * 删除队列中的头部元素，如果队列为空，则返回 null - */ - public dequeue() { - const removeHead = this.linkedList.deleteHead(); - return removeHead ? removeHead.value : null; - } - - public toString(callback?: any) { - return this.linkedList.toString(callback); - } -} diff --git a/packages/graph/src/structs/stack.ts b/packages/graph/src/structs/stack.ts deleted file mode 100644 index af60cb3..0000000 --- a/packages/graph/src/structs/stack.ts +++ /dev/null @@ -1,65 +0,0 @@ -import LinkedList from './linked-list'; - -export default class Stack { - - private linkedList: LinkedList; - - private maxStep: number; - - constructor(maxStep: number = 10) { - this.linkedList = new LinkedList(); - this.maxStep = maxStep; - } - - get length() { - return this.linkedList.toArray().length; - } - - /** - * 判断栈是否为空，如果链表中没有头部元素，则栈为空 - */ - isEmpty() { - return !this.linkedList.head; - } - - /** - * 是否到定义的栈的最大长度，如果达到最大长度后，不再允许入栈 - */ - isMaxStack() { - return this.toArray().length >= this.maxStep; - } - - /** - * 访问顶端元素 - */ - peek() { - if (this.isEmpty()) { - return null; - } - - // 返回头部元素，不删除元素 - return this.linkedList.head.value; - } - - push(value) { - this.linkedList.prepend(value); - if (this.length > this.maxStep) { - this.linkedList.deleteTail(); - } - } - - pop() { - const removeHead = this.linkedList.deleteHead(); - return removeHead ? removeHead.value : null; - } - - toArray() { - return this.linkedList.toArray().map((node) => node.value); - } - - clear() { - while (!this.isEmpty()) { - this.pop(); - } - } -} diff --git a/packages/graph/src/structs/union-find.ts b/packages/graph/src/structs/union-find.ts deleted file mode 100644 index 940ed6d..0000000 --- a/packages/graph/src/structs/union-find.ts +++ /dev/null @@ -1,45 +0,0 @@ -/** - * 并查集 Disjoint set to support quick union - */ -export default class UnionFind { - count: number; - - parent: {}; - - constructor(items: (number | string)[]) { - this.count = items.length; - this.parent = {}; - for (const i of items) { - this.parent[i] = i; - } - } - - // find the root of the item - find(item) { - while (this.parent[item] !== item) { - item = this.parent[item]; - } - return item; - } - - union(a, b) { - const rootA = this.find(a); - const rootB = this.find(b); - - if (rootA === rootB) return; - - // make the element with smaller root the parent - if (rootA < rootB) { - if (this.parent[b] !== b) this.union(this.parent[b], a); - this.parent[b] = this.parent[a]; - } else { - if (this.parent[a] !== a) this.union(this.parent[a], b); - this.parent[a] = this.parent[b]; - } - } - - // whether a and b are connected, i.e. a and b have the same root - connected(a, b) { - return this.find(a) === this.find(b); - } -} diff --git a/packages/graph/src/types.ts b/packages/graph/src/types.ts index 47a03fc..8fe9dce 100644 --- a/packages/graph/src/types.ts +++ b/packages/graph/src/types.ts @@ -1,114 +1,33 @@ +import { Edge, Graph as IGraph, Node, PlainObject } from "@antv/graphlib"; -export type Matrix = number[]; +// 数据集中属性/特征值分布的map +export interface KeyValueMap { + [key:string]: any[]; +} -export interface NodeConfig { - id: string; +export interface NodeData extends PlainObject { clusterId?: string; - [key: string]: any; } -export interface EdgeConfig { - source: string; - target: string; +export interface EdgeData extends PlainObject { weight?: number; - [key: string]: any; -} - -export interface GraphData { - nodes?: NodeConfig[]; - edges?: EdgeConfig[]; } export interface Cluster { id: string; - nodes: NodeConfig[]; + nodes: Node[]; sumTot?: number; } export interface ClusterData { clusters: Cluster[]; - clusterEdges: EdgeConfig[]; + clusterEdges: Edge[]; } export interface ClusterMap { - [key: string]: Cluster + [key: string]: Cluster; } -// 图算法回调方法接口定义 -export interface IAlgorithmCallbacks { - enter?: (param: { current: string; previous: string }) => void; - leave?: (param: { current: string; previous?: string }) => void; - allowTraversal?: (param: { previous?: string; current?: string; next: string }) => boolean; -} - -export interface DegreeType { - [key: string]: { - degree: number; - inDegree: number; - outDegree: number; - } -} - -export enum DistanceType { - EuclideanDistance = 'euclideanDistance', -} - -export interface PlainObject { - [key: string]: any; -} - -// 数据集中属性/特征值分布的map -export interface KeyValueMap { - [key:string]: any[]; -} +export type Graph = IGraph; -export interface IAlgorithm { - getAdjMatrix: (graphData: GraphData, directed?: boolean) => Matrix[], - breadthFirstSearch: ( - graphData: GraphData, - startNodeId: string, - originalCallbacks?: IAlgorithmCallbacks, - directed?: boolean - ) => void, - connectedComponent: (graphData: GraphData, directed?: boolean) => NodeConfig[][], - getDegree: (graphData: GraphData) => DegreeType, - getInDegree: (graphData: GraphData, nodeId: string) => number, - getOutDegree: (graphData: GraphData, nodeId: string) => number, - detectCycle: (graphData: GraphData) => { [key: string]: string }, - detectDirectedCycle: (graphData: GraphData) => { [key: string]: string }, - detectAllCycles: (graphData: GraphData, directed?: boolean, nodeIds?: string[], include?: boolean) => any, - detectAllDirectedCycle: (graphData: GraphData, nodeIds?: string[], include?: boolean) => any, - detectAllUndirectedCycle: (graphData: GraphData, nodeIds?: string[], include?: boolean) => any, - depthFirstSearch: (graphData: GraphData, startNodeId: string, callbacks?: IAlgorithmCallbacks) => void, - dijkstra: (graphData: GraphData, source: string, directed?: boolean, weightPropertyName?: string) => { length: object, allPath: object, path: object}, - findAllPath: (graphData: GraphData, start: string, end: string, directed?: boolean) => any, - findShortestPath: (graphData: GraphData, start: string, end: string, directed?: boolean, weightPropertyName?: string) => any, - floydWarshall: (graphData: GraphData, directed?: boolean) => Matrix[], - labelPropagation: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, maxIteration?: number) => ClusterData, - louvain: (graphData: GraphData, directed: boolean, weightPropertyName: string, threshold: number) => ClusterData, - minimumSpanningTree: (graphData: GraphData, weight?: string, algo?: string) => EdgeConfig[], - pageRank: (graphData: GraphData, epsilon?: number, linkProb?: number) => { [key: string]: number}, - getNeighbors: (nodeId: string, edges?: EdgeConfig[], type?: 'target' | 'source' | undefined) => string[], - Stack: any, - GADDI: (graphData: GraphData, pattern: GraphData, directed: boolean, k: number, length: number, nodeLabelProp: string, edgeLabelProp: string) => GraphData[], - getAdjMatrixAsync: (graphData: GraphData, directed?: boolean) => Matrix[], - connectedComponentAsync: (graphData: GraphData, directed?: boolean) => NodeConfig[][], - getDegreeAsync: (graphData: GraphData) => DegreeType, - getInDegreeAsync: (graphData: GraphData, nodeId: string) => number, - getOutDegreeAsync: (graphData: GraphData, nodeId: string) => number, - detectCycleAsync: (graphData: GraphData) => { [key: string]: string }, - detectDirectedCycleAsync: (graphData: GraphData) => { [key: string]: string }, - detectAllCyclesAsync: (graphData: GraphData, directed?: boolean, nodeIds?: string[], include?: boolean) => any, - detectAllDirectedCycleAsync: (graphData: GraphData, nodeIds?: string[], include?: boolean) => any, - detectAllUndirectedCycleAsync: (graphData: GraphData, nodeIds?: string[], include?: boolean) => any, - dijkstraAsync: (graphData: GraphData, source: string, directed?: boolean, weightPropertyName?: string) => { length: object, allPath: object, path: object}, - findAllPathAsync: (graphData: GraphData, start: string, end: string, directed?: boolean) => any, - findShortestPathAsync: (graphData: GraphData, start: string, end: string, directed?: boolean, weightPropertyName?: string) => any, - floydWarshallAsync: (graphData: GraphData, directed?: boolean) => Matrix[], - labelPropagationAsync: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, maxIteration?: number) => ClusterData, - louvainAsync: (graphData: GraphData, directed: boolean, weightPropertyName: string, threshold: number) => ClusterData, - minimumSpanningTreeAsync: (graphData: GraphData, weight?: string, algo?: string) => EdgeConfig[], - pageRankAsync: (graphData: GraphData, epsilon?: number, linkProb?: number) => { [key: string]: number}, - getNeighborsAsync: (nodeId: string, edges?: EdgeConfig[], type?: 'target' | 'source' | undefined) => string[], - GADDIAsync: (graphData: GraphData, pattern: GraphData, directed: boolean, k: number, length: number, nodeLabelProp: string, edgeLabelProp: string) => GraphData[], -} \ No newline at end of file +export type Matrix = number[]; \ No newline at end of file diff --git a/packages/graph/src/util.ts b/packages/graph/src/util.ts deleted file mode 100644 index 35ae015..0000000 --- a/packages/graph/src/util.ts +++ /dev/null @@ -1,59 +0,0 @@ -import { EdgeConfig, GraphData, Matrix } from './types' - -/** - * 获取指定节点的所有邻居 - * @param nodeId 节点 ID - * @param edges 图中的所有边数据 - * @param type 邻居类型 - */ -export const getNeighbors = (nodeId: string, edges: EdgeConfig[] = [], type?: 'target' | 'source' | undefined): string[] => { - const currentEdges = edges.filter(edge => edge.source === nodeId || edge.target === nodeId) - if (type === 'target') { - // 当前节点为 source，它所指向的目标节点 - const neighhborsConverter = (edge: EdgeConfig) => { - return edge.source === nodeId; - }; - return currentEdges.filter(neighhborsConverter).map((edge) => edge.target); - } - if (type === 'source') { - // 当前节点为 target，它所指向的源节点 - const neighhborsConverter = (edge: EdgeConfig) => { - return edge.target === nodeId; - }; - return currentEdges.filter(neighhborsConverter).map((edge) => edge.source); - } - - // 若未指定 type ，则返回所有邻居 - const neighhborsConverter = (edge: EdgeConfig) => { - return edge.source === nodeId ? edge.target : edge.source; - }; - return currentEdges.map(neighhborsConverter); -} - -/** - * 获取指定节点的出边 - * @param nodeId 节点 ID - * @param edges 图中的所有边数据 - */ -export const getOutEdgesNodeId = (nodeId: string, edges: EdgeConfig[]) => { - return edges.filter(edge => edge.source === nodeId) -} - -/** - * 获取指定节点的边，包括出边和入边 - * @param nodeId 节点 ID - * @param edges 图中的所有边数据 - */ -export const getEdgesByNodeId = (nodeId: string, edges: EdgeConfig[]) => { - return edges.filter(edge => edge.source === nodeId || edge.target === nodeId) -} - -/** - * 生成唯一的 ID，规则是序号 + 时间戳 - * @param index 序号 - */ -export const uniqueId = (index: number = 0) => { - const random1 = `${Math.random()}`.split('.')[1].substr(0, 5); - const random2 = `${Math.random()}`.split('.')[1].substr(0, 5); - return `${index}-${random1}${random2}` -}; diff --git a/packages/graph/src/utils/data-preprocessing.ts b/packages/graph/src/utils.ts similarity index 54% rename from packages/graph/src/utils/data-preprocessing.ts rename to packages/graph/src/utils.ts index fcf54cd..80470f3 100644 --- a/packages/graph/src/utils/data-preprocessing.ts +++ b/packages/graph/src/utils.ts @@ -1,52 +1,48 @@ -import { uniq } from '@antv/util'; -import { PlainObject, DistanceType, GraphData, KeyValueMap } from '../types'; -import Vector from './vector'; +import { Node, PlainObject } from "@antv/graphlib"; +import { KeyValueMap, NodeData } from "./types"; +import { uniq } from "@antv/util"; + +export const getAllProperties = (nodes: Node[]) => { + const allProperties: NodeData[] = []; + nodes.forEach((node) => { + allProperties.push(node.data); + }); + return allProperties; +}; -/** - * 获取数据中所有的属性及其对应的值 - * @param dataList 数据集 - * @param involvedKeys 参与计算的key集合 - * @param uninvolvedKeys 不参与计算的key集合 - */ export const getAllKeyValueMap = (dataList: PlainObject[], involvedKeys?: string[], uninvolvedKeys?: string[]) => { - let keys = []; + let keys: string[] = []; // 指定了参与计算的keys时，使用指定的keys if (involvedKeys?.length) { keys = involvedKeys; } else { // 未指定抽取的keys时，提取数据中所有的key - dataList.forEach(data => { + dataList.forEach((data) => { keys = keys.concat(Object.keys(data)); - }) + }); keys = uniq(keys); } // 获取所有值非空的key的value数组 const allKeyValueMap: KeyValueMap = {}; - keys.forEach(key => { - let value = []; - dataList.forEach(data => { + keys.forEach((key) => { + const value: unknown[] = []; + dataList.forEach((data) => { if (data[key] !== undefined && data[key] !== '') { value.push(data[key]); } - }) + }); if (value.length && !uninvolvedKeys?.includes(key)) { allKeyValueMap[key] = uniq(value); } - }) + }); return allKeyValueMap; -} +}; -/** - * one-hot编码：数据特征提取 - * @param dataList 数据集 - * @param involvedKeys 参与计算的的key集合 - * @param uninvolvedKeys 不参与计算的key集合 - */ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvolvedKeys?: string[]) => { // 获取数据中所有的属性/特征及其对应的值 const allKeyValueMap = getAllKeyValueMap(dataList, involvedKeys, uninvolvedKeys); - const oneHotCode = []; + const oneHotCode: unknown[][] = []; if (!Object.keys(allKeyValueMap).length) { return oneHotCode; } @@ -54,16 +50,16 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol // 获取所有的属性/特征值 const allValue = Object.values(allKeyValueMap); // 是否所有属性/特征的值都是数值型 - const isAllNumber = allValue.every(value => value.every(item => (typeof(item) === 'number'))); + const isAllNumber = allValue.every((value) => value.every((item) => (typeof(item) === 'number'))); // 对数据进行one-hot编码 dataList.forEach((data, index) => { - let code = []; - Object.keys(allKeyValueMap).forEach(key => { + let code: unknown[] = []; + Object.keys(allKeyValueMap).forEach((key) => { const keyValue = data[key]; const allKeyValue = allKeyValueMap[key]; - const valueIndex = allKeyValue.findIndex(value => keyValue === value); - let subCode = []; + const valueIndex = allKeyValue.findIndex((value) => keyValue === value); + const subCode = []; // 如果属性/特征所有的值都能转成数值型，不满足分箱，则直接用值（todo: 为了收敛更快，需做归一化处理） if (isAllNumber) { subCode.push(keyValue); @@ -78,33 +74,8 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol } } code = code.concat(subCode); - }) + }); oneHotCode[index] = code; - }) + }); return oneHotCode; -} - -/** - * getDistance：获取两个元素之间的距离 - * @param item - * @param otherItem - * @param distanceType 距离类型 - * @param graphData 图数据 - */ -export const getDistance = (item, otherItem, distanceType: DistanceType = DistanceType.EuclideanDistance, graphData?: GraphData) => { - let distance = 0; - switch (distanceType) { - case DistanceType.EuclideanDistance: - distance = new Vector(item).euclideanDistance(new Vector(otherItem)); - break; - default: - break; - } - return distance; -} - -export default { - getAllKeyValueMap, - oneHot, - getDistance, -} +}; \ No newline at end of file diff --git a/packages/graph/src/utils/node-properties.ts b/packages/graph/src/utils/node-properties.ts deleted file mode 100644 index 3fc53a7..0000000 --- a/packages/graph/src/utils/node-properties.ts +++ /dev/null @@ -1,78 +0,0 @@ -import { NodeConfig } from '../types'; -import { secondReg, dateReg } from '../constants/time'; - -// 获取所有属性并排序 -export const getAllSortProperties = (nodes: NodeConfig[] = [], n: number = 100) => { - const propertyKeyInfo = {}; - nodes.forEach(node => { - if (!node.properties) { - return; - } - Object.keys(node.properties).forEach(propertyKey => { - // 目前过滤只保留可以转成数值型的或日期型的, todo: 统一转成one-hot特征向量或者embedding - if (propertyKey === 'id' || !`${node.properties[propertyKey]}`.match(secondReg) && - !`${node.properties[propertyKey]}`.match(dateReg) && - isNaN(Number(node.properties[propertyKey]))) { - if (propertyKeyInfo.hasOwnProperty(propertyKey)) { - delete propertyKeyInfo[propertyKey]; - } - return; - } - if (propertyKeyInfo.hasOwnProperty(propertyKey)) { - propertyKeyInfo[propertyKey] += 1; - } else { - propertyKeyInfo[propertyKey] = 1; - } - }) - }) - - // 取top50的属性 - const sortKeys = Object.keys(propertyKeyInfo).sort((a,b) => propertyKeyInfo[b] - propertyKeyInfo[a]); - return sortKeys.length < n ? sortKeys : sortKeys.slice(0, n); -} - -const processProperty = (properties, propertyKeys) => propertyKeys.map(key => { - if (properties.hasOwnProperty(key)) { - // // 可以转成数值的直接转成数值 - // if (!isNaN(Number(properties[key]))) { - // return Number(properties[key]); - // } - // // 时间型的转成时间戳 - // if (properties[key].match(secondReg) || properties[key].match(dateReg)) { - // // @ts-ignore - // return Number(Date.parse(new Date(properties[key]))) / 1000; - // } - return properties[key]; - } - return 0; -}) - -// 获取属性特征权重 -export const getPropertyWeight = (nodes: NodeConfig[]) => { - const propertyKeys = getAllSortProperties(nodes); - let allPropertiesWeight = []; - for (let i = 0; i < nodes.length; i++) { - allPropertiesWeight[i] = processProperty(nodes[i].properties, propertyKeys); - } - return allPropertiesWeight; -} - -// 获取所有节点的属性集合 -export const getAllProperties = (nodes, key = undefined) => { - const allProperties = []; - nodes.forEach(node => { - if (key === undefined) { - allProperties.push(node); - } - if (node[key] !== undefined) { - allProperties.push(node[key]); - } - }) - return allProperties; -} - -export default { - getAllSortProperties, - getPropertyWeight, - getAllProperties -} diff --git a/packages/graph/src/utils/vector.ts b/packages/graph/src/vector.ts similarity index 71% rename from packages/graph/src/utils/vector.ts rename to packages/graph/src/vector.ts index 1b61849..ee65fa9 100644 --- a/packages/graph/src/utils/vector.ts +++ b/packages/graph/src/vector.ts @@ -1,13 +1,12 @@ - /** * 向量运算 */ import { clone } from '@antv/util'; -class Vector { +export class Vector { arr: number[]; - constructor(arr) { + constructor(arr: number[]) { this.arr = arr; } @@ -15,7 +14,7 @@ class Vector { return this.arr || []; } - add(otherVector) { + add(otherVector: Vector) { const otherArr = otherVector.arr; if (!this.arr?.length) { return new Vector(otherArr); @@ -24,15 +23,15 @@ class Vector { return new Vector(this.arr); } if (this.arr.length === otherArr.length) { - let res = []; - for (let index in this.arr) { + const res: number[] = []; + for (const index in this.arr) { res[index] = this.arr[index] + otherArr[index]; } return new Vector(res); } } - subtract(otherVector) { + subtract(otherVector: Vector) { const otherArr = otherVector.arr; if (!this.arr?.length) { return new Vector(otherArr); @@ -41,18 +40,18 @@ class Vector { return new Vector(this.arr); } if (this.arr.length === otherArr.length) { - let res = []; - for (let index in this.arr) { + const res: number[] = []; + for (const index in this.arr) { res[index] = this.arr[index] - otherArr[index]; } return new Vector(res); } } - avg(length) { - let res = []; + avg(length: number) { + const res: number[] = []; if (length !== 0) { - for (let index in this.arr) { + for (const index in this.arr) { res[index] = this.arr[index] / length; } } @@ -60,22 +59,22 @@ class Vector { } negate() { - let res = []; - for (let index in this.arr) { + const res: number[] = []; + for (const index in this.arr) { res[index] = - this.arr[index]; } return new Vector(res); } // 平方欧式距离 - squareEuclideanDistance(otherVector) { + squareEuclideanDistance(otherVector: Vector) { const otherArr = otherVector.arr; if (!this.arr?.length || !otherArr?.length) { return 0; } if (this.arr.length === otherArr.length) { let res = 0; - for (let index in this.arr) { + for (const index in this.arr) { res += Math.pow(this.arr[index] - otherVector.arr[index], 2); } return res; @@ -83,30 +82,30 @@ class Vector { } // 欧式距离 - euclideanDistance(otherVector) { + euclideanDistance(otherVector: Vector) { const otherArr = otherVector.arr; if (!this.arr?.length || !otherArr?.length) { return 0; } if (this.arr.length === otherArr.length) { let res = 0; - for (let index in this.arr) { + for (const index in this.arr) { res += Math.pow(this.arr[index] - otherVector.arr[index], 2); } return Math.sqrt(res); - } else { - console.error('The two vectors are unequal in length.') - } + } + console.error('The two vectors are unequal in length.'); + } // 归一化处理 normalize() { - let res = []; + const res: number[] = []; const cloneArr = clone(this.arr); - cloneArr.sort((a, b) => a - b); + cloneArr.sort((a: number, b: number) => a - b); const max = cloneArr[cloneArr.length - 1]; const min = cloneArr[0]; - for (let index in this.arr) { + for (const index in this.arr) { res[index] = (this.arr[index] - min) / (max - min); } return new Vector(res); @@ -118,36 +117,36 @@ class Vector { return 0; } let res = 0; - for (let index in this.arr) { + for (const index in this.arr) { res += Math.pow(this.arr[index], 2); } return Math.sqrt(res); } // 两个向量的点积 - dot(otherVector) { + dot(otherVector: Vector) { const otherArr = otherVector.arr; if (!this.arr?.length || !otherArr?.length) { return 0; } if (this.arr.length === otherArr.length) { let res = 0; - for (let index in this.arr) { + for (const index in this.arr) { res += this.arr[index] * otherVector.arr[index]; } return res; - } else { - console.error('The two vectors are unequal in length.') - } + } + console.error('The two vectors are unequal in length.'); + } // 两个向量比较 - equal(otherVector) { + equal(otherVector: Vector) { const otherArr = otherVector.arr; if (this.arr?.length !== otherArr?.length) { return false; } - for (let index in this.arr) { + for (const index in this.arr) { if (this.arr[index] !== otherArr[index]) { return false; } @@ -155,5 +154,3 @@ class Vector { return true; } } - -export default Vector; diff --git a/packages/graph/src/workers/algorithm.ts b/packages/graph/src/workers/algorithm.ts deleted file mode 100644 index acade39..0000000 --- a/packages/graph/src/workers/algorithm.ts +++ /dev/null @@ -1,16 +0,0 @@ -export { default as getAdjMatrix } from '../adjacent-matrix'; -export { default as breadthFirstSearch } from '../bfs'; -export { default as connectedComponent } from '../connected-component'; -export { default as getDegree } from '../degree'; -export { getInDegree, getOutDegree } from '../degree'; -export { default as detectCycle } from '../detect-cycle'; -export { default as depthFirstSearch } from '../dfs'; -export { default as dijkstra } from '../dijkstra'; -export { findAllPath, findShortestPath } from '../find-path'; -export { default as floydWarshall } from '../floydWarshall'; -export { default as labelPropagation } from '../label-propagation'; -export { default as louvain } from '../louvain'; -export { default as minimumSpanningTree } from '../mts'; -export { default as pageRank } from '../pageRank'; -export { default as GADDI } from '../gaddi'; -export { getNeighbors } from '../util'; diff --git a/packages/graph/src/workers/constant.ts b/packages/graph/src/workers/constant.ts deleted file mode 100644 index cceaec6..0000000 --- a/packages/graph/src/workers/constant.ts +++ /dev/null @@ -1,31 +0,0 @@ -export const ALGORITHM = { - pageRank: 'pageRank', - breadthFirstSearch: 'breadthFirstSearch', - connectedComponent: 'connectedComponent', - depthFirstSearch: 'depthFirstSearch', - detectCycle: 'detectCycle', - detectDirectedCycle: 'detectDirectedCycle', - detectAllCycles: 'detectAllCycles', - detectAllDirectedCycle: 'detectAllDirectedCycle', - detectAllUndirectedCycle: 'detectAllUndirectedCycle', - dijkstra: 'dijkstra', - findAllPath: 'findAllPath', - findShortestPath: 'findShortestPath', - floydWarshall: 'floydWarshall', - getAdjMatrix: 'getAdjMatrix', - getDegree: 'getDegree', - getInDegree: 'getInDegree', - getNeighbors: 'getNeighbors', - getOutDegree: 'getOutDegree', - labelPropagation: 'labelPropagation', - louvain: 'louvain', - GADDI: 'GADDI', - minimumSpanningTree: 'minimumSpanningTree', - SUCCESS: 'SUCCESS', - FAILURE: 'FAILURE', -}; - -export const MESSAGE = { - SUCCESS: 'SUCCESS', - FAILURE: 'FAILURE', -}; diff --git a/packages/graph/src/workers/createWorker.ts b/packages/graph/src/workers/createWorker.ts deleted file mode 100644 index 37f8112..0000000 --- a/packages/graph/src/workers/createWorker.ts +++ /dev/null @@ -1,33 +0,0 @@ -import { MESSAGE } from './constant'; -import Worker from './index.worker'; - -interface Event { - type: string; - data: any; -} - -/** - * 创建一个在worker中运行的算法 - * @param type 算法类型 - */ -const createWorker = (type: string) => (...data) => - new Promise((resolve, reject) => { - const worker = new Worker(); - worker.postMessage({ - _algorithmType:type, - data, - }); - - worker.onmessage = (event: Event) => { - const { data, _algorithmType } = event.data; - if (MESSAGE.SUCCESS === _algorithmType) { - resolve(data); - } else { - reject(); - } - - worker.terminate(); - }; - }); - -export default createWorker; diff --git a/packages/graph/src/workers/index.ts b/packages/graph/src/workers/index.ts deleted file mode 100644 index 467a320..0000000 --- a/packages/graph/src/workers/index.ts +++ /dev/null @@ -1,253 +0,0 @@ -import { - GraphData, - DegreeType, - Matrix, - ClusterData, - EdgeConfig, - NodeConfig, -} from '../types'; -import createWorker from './createWorker'; -import { ALGORITHM } from './constant'; - -/** - * @param graphData 图数据 - * @param directed 是否为有向图 - */ -const getAdjMatrixAsync = (graphData: GraphData, directed?: boolean) => - createWorker(ALGORITHM.getAdjMatrix)(...[graphData, directed]); - -/** - * 图的连通分量 - * @param graphData 图数据 - * @param directed 是否为有向图 - */ -const connectedComponentAsync = (graphData: GraphData, directed?: boolean) => - createWorker(ALGORITHM.connectedComponent)(...[graphData, directed]); - -/** - * 获取节点的度 - * @param graphData 图数据 - */ -const getDegreeAsync = (graphData: GraphData) => - createWorker(ALGORITHM.getDegree)(graphData); - -/** - * 获取节点的入度 - * @param graphData 图数据 - * @param nodeId 节点ID - */ -const getInDegreeAsync = (graphData: GraphData, nodeId: string) => - createWorker(ALGORITHM.getInDegree)(graphData, nodeId); - -/** - * 获取节点的出度 - * @param graphData 图数据 - * @param nodeId 节点ID - */ -const getOutDegreeAsync = (graphData: GraphData, nodeId: string) => - createWorker(ALGORITHM.getOutDegree)(graphData, nodeId); - -/** - * 检测图中的(有向) Cycle - * @param graphData 图数据 - */ -const detectCycleAsync = (graphData: GraphData) => - createWorker<{ - [key: string]: string; - }>(ALGORITHM.detectCycle)(graphData); - -/** - * 检测图中的(无向) Cycle - * @param graphData 图数据 - */ - const detectAllCyclesAsync = (graphData: GraphData) => - createWorker<{ - [key: string]: string; - }>(ALGORITHM.detectAllCycles)(graphData); - -/** - * 检测图中的所有(有向) Cycle - * @param graphData 图数据 - */ - const detectAllDirectedCycleAsync = (graphData: GraphData) => - createWorker<{ - [key: string]: string; - }>(ALGORITHM.detectAllDirectedCycle)(graphData); - -/** - * 检测图中的所有(无向) Cycle - * @param graphData 图数据 - */ - const detectAllUndirectedCycleAsync = (graphData: GraphData) => - createWorker<{ - [key: string]: string; - }>(ALGORITHM.detectAllUndirectedCycle)(graphData); - -/** - * Dijkstra's algorithm, See {@link https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm} - * @param graphData 图数据 - */ -const dijkstraAsync = ( - graphData: GraphData, - source: string, - directed?: boolean, - weightPropertyName?: string, -) => - createWorker<{ - length: number; - path: any; - allPath: any; - }>(ALGORITHM.dijkstra)(...[graphData, source, directed, weightPropertyName]); - -/** - * 查找两点之间的所有路径 - * @param graphData 图数据 - * @param start 路径起始点ID - * @param end 路径终点ID - * @param directed 是否为有向图 - */ -const findAllPathAsync = (graphData: GraphData, start: string, end: string, directed?: boolean) => - createWorker(ALGORITHM.findAllPath)(...[graphData, start, end, directed]); - -/** - * 查找两点之间的所有路径 - * @param graphData 图数据 - * @param start 路径起始点ID - * @param end 路径终点ID - * @param directed 是否为有向图 - * @param weightPropertyName 边权重的属名称，若数据中没有权重，则默认每条边权重为 1 - */ -const findShortestPathAsync = ( - graphData: GraphData, - start: string, - end: string, - directed?: boolean, - weightPropertyName?: string, -) => - createWorker<{ - length: number; - path: any; - allPath: any; - }>(ALGORITHM.findShortestPath)(...[graphData, start, end, directed, weightPropertyName]); - -/** - * Floyd–Warshall algorithm, See {@link https://en.wikipedia.org/wiki/Floyd%E2%80%93Warshall_algorithm} - * @param graphData 图数据 - * @param directed 是否为有向图 - */ -const floydWarshallAsync = (graphData: GraphData, directed?: boolean) => - createWorker(ALGORITHM.floydWarshall)(...[graphData, directed]); - -/** - * 标签传播算法 - * @param graphData 图数据 - * @param directed 是否有向图，默认为 false - * @param weightPropertyName 权重的属性字段 - * @param maxIteration 最大迭代次数 - */ -const labelPropagationAsync = ( - graphData: GraphData, - directed: boolean, - weightPropertyName: string, - maxIteration: number = 1000, -) => - createWorker(ALGORITHM.labelPropagation)( - graphData, - directed, - weightPropertyName, - maxIteration, - ); - -/** - * 社区发现 louvain 算法 - * @param graphData 图数据 - * @param directed 是否有向图，默认为 false - * @param weightPropertyName 权重的属性字段 - * @param threshold - */ -const louvainAsync = ( - graphData: GraphData, - directed: boolean, - weightPropertyName: string, - threshold: number, -) => - createWorker(ALGORITHM.louvain)(graphData, directed, weightPropertyName, threshold); - -/** - * 最小生成树，See {@link https://en.wikipedia.org/wiki/Kruskal%27s_algorithm} - * @param graph - * @param weight 指定用于作为边权重的属性，若不指定，则认为所有边权重一致 - * @param algo 'prim' | 'kruskal' 算法类型 - * @return EdgeConfig[] 返回构成MST的边的数组 - */ -const minimumSpanningTreeAsync = (graphData: GraphData, weight?: boolean, algo?: string) => - createWorker(ALGORITHM.minimumSpanningTree)(...[graphData, weight, algo]); - -/** - * PageRank https://en.wikipedia.org/wiki/PageRank - * refer: https://github.com/anvaka/ngraph.pagerank - * @param graph - * @param epsilon 判断是否收敛的精度值，默认 0.000001 - * @param linkProb 阻尼系数（dumping factor），指任意时刻，用户访问到某节点后继续访问该节点链接的下一个节点的概率，经验值 0.85 - */ -const pageRankAsync = (graphData: GraphData, epsilon?: number, linkProb?: number) => - createWorker<{ - [key: string]: number; - }>(ALGORITHM.pageRank)(...[graphData, epsilon, linkProb]); - -/** - * 获取指定节点的所有邻居 - * @param nodeId 节点 ID - * @param edges 图中的所有边数据 - * @param type 邻居类型 - */ -const getNeighborsAsync = ( - nodeId: string, - edges: EdgeConfig[], - type?: 'target' | 'source' | undefined, -) => createWorker(ALGORITHM.getNeighbors)(...[nodeId, edges, type]); - -/** - * GADDI 图模式匹配 - * @param graphData 原图数据 - * @param pattern 搜索图（需要在原图上搜索的模式）数据 - * @param directed 是否计算有向图，默认 false - * @param k 参数 k，表示 k-近邻 - * @param length 参数 length - * @param nodeLabelProp 节点数据中代表节点标签（分类信息）的属性名。默认为 cluster - * @param edgeLabelProp 边数据中代表边标签（分类信息）的属性名。默认为 cluster - */ -const GADDIAsync = ( - graphData: GraphData, - pattern: GraphData, - directed: boolean = false, - k: number, - length: number, - nodeLabelProp: string = 'cluster', - edgeLabelProp: string = 'cluster', -) => - createWorker(ALGORITHM.GADDI)( - ...[graphData, pattern, directed, k, length, nodeLabelProp, edgeLabelProp], - ); - -export { - getAdjMatrixAsync, - connectedComponentAsync, - getDegreeAsync, - getInDegreeAsync, - getOutDegreeAsync, - detectCycleAsync, - detectAllCyclesAsync, - detectAllDirectedCycleAsync, - detectAllUndirectedCycleAsync, - dijkstraAsync, - findAllPathAsync, - findShortestPathAsync, - floydWarshallAsync, - labelPropagationAsync, - louvainAsync, - minimumSpanningTreeAsync, - pageRankAsync, - getNeighborsAsync, - GADDIAsync, -}; diff --git a/packages/graph/src/workers/index.worker.ts b/packages/graph/src/workers/index.worker.ts deleted file mode 100644 index dbdb581..0000000 --- a/packages/graph/src/workers/index.worker.ts +++ /dev/null @@ -1,27 +0,0 @@ -import * as algorithm from './algorithm'; -import { MESSAGE } from './constant'; - -const ctx: Worker = (typeof self !== 'undefined') ? self : {} as any; - -interface Event { - type: string; - data: any; -} - -ctx.onmessage = (event: Event) => { - const { _algorithmType, data } = event.data; - // 如果发送内容没有私有类型。说明不是自己发的。不管 - // fix: https://github.com/antvis/algorithm/issues/25 - if(!_algorithmType){ - return; - } - if (typeof algorithm[_algorithmType] === 'function') { - const result = algorithm[_algorithmType](...data); - ctx.postMessage({ _algorithmType: MESSAGE.SUCCESS, data: result }); - return; - } - ctx.postMessage({ _algorithmType: MESSAGE.FAILURE }); -}; - -// https://stackoverflow.com/questions/50210416/webpack-worker-loader-fails-to-compile-typescript-worker -export default null as any; diff --git a/packages/graph/tests/unit/adjacent-matrix-async-spec.ts b/packages/graph/tests/unit/adjacent-matrix-async-spec.ts deleted file mode 100644 index ccdd4a7..0000000 --- a/packages/graph/tests/unit/adjacent-matrix-async-spec.ts +++ /dev/null @@ -1,116 +0,0 @@ -import { getAlgorithm } from './utils'; - -const data = { - nodes: [ - { - id: 'A', - label: '0', - }, - { - id: 'B', - label: '1', - }, - { - id: 'C', - label: '2', - }, - { - id: 'D', - label: '3', - }, - { - id: 'E', - label: '4', - }, - { - id: 'F', - label: '5', - }, - { - id: 'G', - label: '6', - }, - { - id: 'H', - label: '7', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'G', - }, - { - source: 'A', - target: 'D', - }, - { - source: 'A', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - ], -}; - -describe('(Async) Adjacency Matrix', () => { - it('undirected', async () => { - const { getAdjMatrixAsync } = await getAlgorithm(); - const matrix = await getAdjMatrixAsync(data); - expect(Object.keys(matrix).length).toBe(8); - const node0Adj = matrix[0]; - expect(node0Adj.length).toBe(5); - expect(node0Adj[0]).toBe(undefined); - expect(node0Adj[1]).toBe(1); - expect(node0Adj[2]).toBe(undefined); - expect(node0Adj[3]).toBe(1); - expect(node0Adj[4]).toBe(1); - - const node1Adj = matrix[1]; - expect(node1Adj.length).toBe(3); - expect(node1Adj[0]).toBe(1); - expect(node1Adj[1]).toBe(undefined); - expect(node1Adj[2]).toBe(1); - - const node5Adj = matrix[5]; - expect(node5Adj.length).toBe(5); - expect(node5Adj[0]).toBe(undefined); - expect(node5Adj[1]).toBe(undefined); - expect(node5Adj[2]).toBe(undefined); - expect(node5Adj[3]).toBe(1); - expect(node5Adj[4]).toBe(1); - }); - - it('directed', async () => { - const { getAdjMatrixAsync } = await getAlgorithm(); - const matrix = await getAdjMatrixAsync(data, true); - expect(Object.keys(matrix).length).toBe(8); - const node0Adj = matrix[0]; - expect(node0Adj.length).toBe(5); - expect(node0Adj[0]).toBe(undefined); - expect(node0Adj[1]).toBe(1); - expect(node0Adj[2]).toBe(undefined); - expect(node0Adj[3]).toBe(1); - expect(node0Adj[4]).toBe(1); - - const node1Adj = matrix[1]; - expect(node1Adj.length).toBe(3); - expect(node1Adj[0]).toBe(undefined); - expect(node1Adj[1]).toBe(undefined); - expect(node1Adj[2]).toBe(1); - }); -}); diff --git a/packages/graph/tests/unit/adjacent-matrix-spec.ts b/packages/graph/tests/unit/adjacent-matrix-spec.ts deleted file mode 100644 index ec485a8..0000000 --- a/packages/graph/tests/unit/adjacent-matrix-spec.ts +++ /dev/null @@ -1,114 +0,0 @@ -import { getAdjMatrix } from '../../src'; - -const data = { - nodes: [ - { - id: 'A', - label: '0', - }, - { - id: 'B', - label: '1', - }, - { - id: 'C', - label: '2', - }, - { - id: 'D', - label: '3', - }, - { - id: 'E', - label: '4', - }, - { - id: 'F', - label: '5', - }, - { - id: 'G', - label: '6', - }, - { - id: 'H', - label: '7', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'G', - }, - { - source: 'A', - target: 'D', - }, - { - source: 'A', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - ], -}; - -describe('Adjacency Matrix', () => { - it('undirected', () => { - const matrix = getAdjMatrix(data); - expect(Object.keys(matrix).length).toBe(8); - const node0Adj = matrix[0]; - expect(node0Adj.length).toBe(5); - expect(node0Adj[0]).toBe(undefined); - expect(node0Adj[1]).toBe(1); - expect(node0Adj[2]).toBe(undefined); - expect(node0Adj[3]).toBe(1); - expect(node0Adj[4]).toBe(1); - - const node1Adj = matrix[1]; - expect(node1Adj.length).toBe(3); - expect(node1Adj[0]).toBe(1); - expect(node1Adj[1]).toBe(undefined); - expect(node1Adj[2]).toBe(1); - - const node5Adj = matrix[5]; - expect(node5Adj.length).toBe(5); - expect(node5Adj[0]).toBe(undefined); - expect(node5Adj[1]).toBe(undefined); - expect(node5Adj[2]).toBe(undefined); - expect(node5Adj[3]).toBe(1); - expect(node5Adj[4]).toBe(1); - }); - - it('directed', () => { - const matrix = getAdjMatrix(data, true); - expect(Object.keys(matrix).length).toBe(8); - const node0Adj = matrix[0]; - expect(node0Adj.length).toBe(5); - expect(node0Adj[0]).toBe(undefined); - expect(node0Adj[1]).toBe(1); - expect(node0Adj[2]).toBe(undefined); - expect(node0Adj[3]).toBe(1); - expect(node0Adj[4]).toBe(1); - - const node1Adj = matrix[1]; - expect(node1Adj.length).toBe(3); - expect(node1Adj[0]).toBe(undefined); - expect(node1Adj[1]).toBe(undefined); - expect(node1Adj[2]).toBe(1); - }); -}); diff --git a/packages/graph/tests/unit/bfs-spec.ts b/packages/graph/tests/unit/bfs-spec.ts deleted file mode 100644 index 68a5c8c..0000000 --- a/packages/graph/tests/unit/bfs-spec.ts +++ /dev/null @@ -1,175 +0,0 @@ -import { breadthFirstSearch } from '../../src'; - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - { - id: 'H', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'G', - }, - { - source: 'A', - target: 'D', - }, - { - source: 'A', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - ], -}; - -describe('breadthFirstSearch', () => { - it('should perform BFS operation on graph', () => { - const enterNodeCallback = jest.fn(); - const leaveNodeCallback = jest.fn(); - - // Traverse graphs without callbacks first. - breadthFirstSearch(data, 'A'); - - // Traverse graph with enterNode and leaveNode callbacks. - breadthFirstSearch(data, 'A', { - enter: enterNodeCallback, - leave: leaveNodeCallback, - }); - - expect(enterNodeCallback).toHaveBeenCalledTimes(7); - expect(leaveNodeCallback).toHaveBeenCalledTimes(7); - - const nodeA = 'A'; - const nodeB = 'B'; - const nodeC = 'C'; - const nodeD = 'D'; - const nodeE = 'E'; - const nodeF = 'F'; - const nodeG = 'G'; - - const enterNodeParamsMap = [ - { currentNode: nodeA, previousNode: '' }, - { currentNode: nodeB, previousNode: nodeA }, - { currentNode: nodeD, previousNode: nodeB }, - { currentNode: nodeE, previousNode: nodeD }, - { currentNode: nodeC, previousNode: nodeE }, - { currentNode: nodeF, previousNode: nodeC }, - { currentNode: nodeG, previousNode: nodeF }, - ]; - - for (let callIndex = 0; callIndex < 6; callIndex += 1) { - const params = enterNodeCallback.mock.calls[callIndex][0]; - expect(params.current).toEqual(enterNodeParamsMap[callIndex].currentNode); - expect(params.previous).toEqual( - enterNodeParamsMap[callIndex].previousNode && - enterNodeParamsMap[callIndex].previousNode, - ); - } - - const leaveNodeParamsMap = [ - { currentNode: nodeA, previousNode: '' }, - { currentNode: nodeB, previousNode: nodeA }, - { currentNode: nodeD, previousNode: nodeB }, - { currentNode: nodeE, previousNode: nodeD }, - { currentNode: nodeC, previousNode: nodeE }, - { currentNode: nodeF, previousNode: nodeC }, - { currentNode: nodeG, previousNode: nodeF }, - ]; - - for (let callIndex = 0; callIndex < 6; callIndex += 1) { - const params = leaveNodeCallback.mock.calls[callIndex][0]; - expect(params.current).toEqual(leaveNodeParamsMap[callIndex].currentNode); - expect(params.previous).toEqual( - leaveNodeParamsMap[callIndex].previousNode && - leaveNodeParamsMap[callIndex].previousNode, - ); - } - }); - - it('should allow to create custom node visiting logic', () => { - - const enterNodeCallback = jest.fn(); - const leaveNodeCallback = jest.fn(); - - // Traverse graph with enterNode and leaveNode callbacks. - breadthFirstSearch(data, 'A', { - enter: enterNodeCallback, - leave: leaveNodeCallback, - allowTraversal: ({ current, next }) => { - return !(current === 'A' && next === 'B'); - }, - }); - - expect(enterNodeCallback).toHaveBeenCalledTimes(5); - expect(leaveNodeCallback).toHaveBeenCalledTimes(5); - - const enterNodeParamsMap = [ - { currentNode: 'A', previousNode: '' }, - { currentNode: 'D', previousNode: 'A' }, - { currentNode: 'E', previousNode: 'D' }, - { currentNode: 'F', previousNode: 'E' }, - { currentNode: 'D', previousNode: 'F' }, - ]; - - for (let callIndex = 0; callIndex < 5; callIndex += 1) { - const params = enterNodeCallback.mock.calls[callIndex][0]; - expect(params.current).toEqual(enterNodeParamsMap[callIndex].currentNode); - expect(params.previous).toEqual( - enterNodeParamsMap[callIndex].previousNode, - ); - } - - const leaveNodeParamsMap = [ - { currentNode: 'A', previousNode: '' }, - { currentNode: 'D', previousNode: 'A' }, - { currentNode: 'E', previousNode: 'D' }, - { currentNode: 'F', previousNode: 'E' }, - { currentNode: 'D', previousNode: 'F' }, - ]; - - for (let callIndex = 0; callIndex < 5; callIndex += 1) { - const params = leaveNodeCallback.mock.calls[callIndex][0]; - expect(params.current).toEqual(leaveNodeParamsMap[callIndex].currentNode); - expect(params.previous).toEqual( - leaveNodeParamsMap[callIndex].previousNode, - ); - } - }); -}); diff --git a/packages/graph/tests/unit/connected-component-async-spec.ts b/packages/graph/tests/unit/connected-component-async-spec.ts deleted file mode 100644 index bea990a..0000000 --- a/packages/graph/tests/unit/connected-component-async-spec.ts +++ /dev/null @@ -1,112 +0,0 @@ -import { getAlgorithm } from './utils'; - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - { - id: 'H', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'A', - target: 'C', - }, - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - { - source: 'G', - target: 'H', - }, - { - source: 'H', - target: 'G', - }, - ], -}; - -describe('(Async) find connected components', () => { - it('detect strongly connected components in undirected graph', async done => { - const { connectedComponentAsync } = await getAlgorithm(); - let result = await connectedComponentAsync(data, false); - expect(result.length).toEqual(2); - expect(result[0].map(node => node.id).sort()).toEqual(['A', 'B', 'C', 'D', 'E', 'F']); - expect(result[1].map(node => node.id).sort()).toEqual(['G', 'H']); - done(); - }); - - it('detect strongly connected components in directed graph', async done => { - const { connectedComponentAsync } = await getAlgorithm(); - let result = await connectedComponentAsync(data, true); - expect(result.length).toEqual(5); - expect(result[3].map(node => node.id).sort()).toEqual(['D', 'E', 'F']); - expect(result[4].map(node => node.id).sort()).toEqual(['G', 'H']); - done(); - }); - - it('test connected components detection performance using large graph', async done => { - fetch('https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json') - .then(res => res.json()) - .then(async data => { - const { connectedComponentAsync } = await getAlgorithm(); - - data.nodes.forEach(node => { - node.label = node.olabel; - node.degree = 0; - data.edges.forEach(edge => { - if (edge.source === node.id || edge.target === node.id) { - node.degree++; - } - }); - }); - - let directedComps = await connectedComponentAsync(data, true); - let undirectedComps = await connectedComponentAsync(data, false); - expect(directedComps.length).toEqual(1589); - expect(undirectedComps.length).toEqual(396); - done(); - }); - }); -}); diff --git a/packages/graph/tests/unit/connected-component-spec.ts b/packages/graph/tests/unit/connected-component-spec.ts deleted file mode 100644 index abf7346..0000000 --- a/packages/graph/tests/unit/connected-component-spec.ts +++ /dev/null @@ -1,106 +0,0 @@ -import { connectedComponent } from '../../src' - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - { - id: 'H', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'A', - target: 'C', - }, - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - { - source: 'G', - target: 'H', - }, - { - source: 'H', - target: 'G', - }, - ], -}; - -describe('find connected components', () => { - - it('detect strongly connected components in undirected graph', () => { - let result = connectedComponent(data, false); - expect(result.length).toEqual(2); - expect(result[0].map((node) => node.id).sort()).toEqual(['A', 'B', 'C', 'D', 'E', 'F']); - expect(result[1].map((node) => node.id).sort()).toEqual(['G', 'H']); - }); - - it('detect strongly connected components in directed graph', () => { - let result = connectedComponent(data, true); - expect(result.length).toEqual(5); - expect(result[3].map((node) => node.id).sort()).toEqual(['D', 'E', 'F']); - expect(result[4].map((node) => node.id).sort()).toEqual(['G', 'H']); - }); - - it('test connected components detection performance using large graph', () => { - fetch('https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json') - .then((res) => res.json()) - .then((data) => { - data.nodes.forEach((node) => { - node.label = node.olabel; - node.degree = 0; - data.edges.forEach((edge) => { - if (edge.source === node.id || edge.target === node.id) { - node.degree++; - } - }); - }); - - let directedComps = connectedComponent(data, true); - let undirectedComps = connectedComponent(data, false); - expect(directedComps.length).toEqual(1589); - expect(undirectedComps.length).toEqual(396); - }); - }); -}); diff --git a/packages/graph/tests/unit/cosineSimilarity-spec.ts b/packages/graph/tests/unit/cosineSimilarity-spec.ts deleted file mode 100644 index 3824680..0000000 --- a/packages/graph/tests/unit/cosineSimilarity-spec.ts +++ /dev/null @@ -1,41 +0,0 @@ -import { cosineSimilarity } from '../../src'; - -describe('cosineSimilarity abnormal demo: ', () => { - it('item contains only zeros: ', () => { - const item = [0, 0, 0]; - const targetTtem = [3, 1, 1]; - const cosineSimilarityValue = cosineSimilarity(item, targetTtem); - expect(cosineSimilarityValue).toBe(0); - }); - it('targetTtem contains only zeros: ', () => { - const item = [3, 5, 2]; - const targetTtem = [0, 0, 0]; - const cosineSimilarityValue = cosineSimilarity(item, targetTtem); - expect(cosineSimilarityValue).toBe(0); - }); - it('item and targetTtem both contains only zeros: ', () => { - const item = [0, 0, 0]; - const targetTtem = [0, 0, 0]; - const cosineSimilarityValue = cosineSimilarity(item, targetTtem); - expect(cosineSimilarityValue).toBe(0); - }); -}); - -describe('cosineSimilarity normal demo: ', () => { - it('demo similar: ', () => { - const item = [30, 0, 100]; - const targetTtem = [32, 1, 120]; - const cosineSimilarityValue = cosineSimilarity(item, targetTtem); - expect(cosineSimilarityValue).toBeGreaterThanOrEqual(0); - expect(cosineSimilarityValue).toBeLessThan(1); - expect(Number(cosineSimilarityValue.toFixed(3))).toBe(0.999); - }); - it('demo dissimilar: ', () => { - const item = [10, 300, 2]; - const targetTtem = [1, 2, 30]; - const cosineSimilarityValue = cosineSimilarity(item, targetTtem); - expect(cosineSimilarityValue).toBeGreaterThanOrEqual(0); - expect(cosineSimilarityValue).toBeLessThan(1); - expect(Number(cosineSimilarityValue.toFixed(3))).toBe(0.074); - }); -}); diff --git a/packages/graph/tests/unit/data/cluster-origin-data.json b/packages/graph/tests/unit/data/cluster-origin-data.json deleted file mode 100644 index 9e26dfe..0000000 --- a/packages/graph/tests/unit/data/cluster-origin-data.json +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file diff --git a/packages/graph/tests/unit/data/cluster-origin-properties-data.json b/packages/graph/tests/unit/data/cluster-origin-properties-data.json deleted file mode 100644 index e3349f2..0000000 --- a/packages/graph/tests/unit/data/cluster-origin-properties-data.json +++ /dev/null @@ -1,370 +0,0 @@ -{ - "nodes": [ - { - "id": "node-0", - "label": "node-0", - "properties": { - "city": "110000", - "age": 20, - "amount": 100, - "wifi": "wifi-0" - } - }, - { - "id": "node-1", - "label": "node-1", - "properties": { - "city": "110000", - "age": 22, - "amount": 100, - "wifi": "wifi-1" - } - }, - { - "id": "node-2", - "label": "node-2", - "properties": { - "city": "110000", - "age": 20, - "amount": 100, - "wifi": "wifi-2" - } - }, - { - "id": "node-3", - "label": "node-3", - "properties": { - "city": "110000", - "age": 21, - "amount": 100, - "wifi": "wifi-3" - } - }, - { - "id": "node-4", - "label": "node-4", - "properties": { - "city": "110000", - "age": 22, - "amount": 100, - "wifi": "wifi-4" - } - }, - { - "id": "node-5", - "label": "node-5", - "properties": { - "city": "310000", - "age": 30, - "amount": 1000, - "wifi": "wifi-5" - } - }, - { - "id": "node-6", - "label": "node-6", - "properties": { - "city": "310000", - "age": 31, - "amount": 1000, - "wifi": "wifi-6" - } - }, - { - "id": "node-7", - "label": "node-7", - "properties": { - "city": "310000", - "age": 31, - "amount": 1000, - "wifi": "wifi-7" - } - }, - { - "id": "node-8", - "label": "node-8", - "properties": { - "city": "310000", - "age": 32, - "amount": 1000, - "wifi": "wifi-8" - } - }, - { - "id": "node-9", - "label": "node-9", - "properties": { - "city": "310000", - "age": 30, - "amount": 1000, - "wifi": "wifi-9" - } - }, - { - "id": "node-10", - "label": "node-10", - "properties": { - "city": "310000", - "age": 30, - "amount": 1000, - "wifi": "wifi-10" - } - }, - { - "id": "node-11", - "label": "node-11", - "properties": { - "city": "440300", - "age": 40, - "amount": 10000, - "wifi": "wifi-11" - } - }, - { - "id": "node-12", - "label": "node-12", - "properties": { - "city": "440300", - "age": 40, - "amount": 10000, - "wifi": "wifi-12" - } - }, - { - "id": "node-13", - "label": "node-13", - "properties": { - "city": "440300", - "age": 41, - "amount": 10000, - "wifi": "wifi-13" - } - }, - { - "id": "node-14", - "label": "node-14", - "properties": { - "city": "440300", - "age": 42, - "amount": 10000, - "wifi": "wifi-14" - } - }, - { - "id": "node-15", - "label": "node-15", - "properties": { - "city": "440300", - "age": 41, - "amount": 10000, - "wifi": "wifi-15" - } - }, - { - "id": "node-16", - "label": "node-16", - "properties": { - "city": "440300", - "age": 43, - "amount": 10000, - "wifi": "wifi-16" - } - } - ], - "edges": [ - { - "id": "edge-0", - "source": "node-0", - "target": "node-1" - }, - { - "id": "edge-1", - "source": "node-0", - "target": "node-2" - }, - { - "id": "edge-2", - "source": "node-0", - "target": "node-3" - }, - { - "id": "edge-3", - "source": "node-0", - "target": "node-4" - }, - { - "id": "edge-4", - "source": "node-1", - "target": "node-2" - }, - { - "id": "edge-5", - "source": "node-1", - "target": "node-3" - }, - { - "id": "edge-6", - "source": "node-1", - "target": "node-4" - }, - { - "id": "edge-7", - "source": "node-2", - "target": "node-3" - }, - { - "id": "edge-8", - "source": "node-2", - "target": "node-4" - }, - { - "id": "edge-9", - "source": "node-3", - "target": "node-4" - }, - - - { - "id": "edge-10", - "source": "node-5", - "target": "node-6" - }, - { - "id": "edge-11", - "source": "node-5", - "target": "node-7" - }, - { - "id": "edge-12", - "source": "node-5", - "target": "node-8" - }, - { - "id": "edge-13", - "source": "node-5", - "target": "node-9" - }, - { - "id": "edge-14", - "source": "node-6", - "target": "node-7" - }, - { - "id": "edge-15", - "source": "node-6", - "target": "node-8" - }, - { - "id": "edge-16", - "source": "node-6", - "target": "node-9" - }, - { - "id": "edge-17", - "source": "node-7", - "target": "node-8" - }, - { - "id": "edge-18", - "source": "node-7", - "target": "node-9" - }, - { - "id": "edge-19", - "source": "node-8", - "target": "node-9" - }, - { - "id": "edge-20", - "source": "node-9", - "target": "node-10" - }, - { - "id": "edge-40", - "source": "node-10", - "target": "node-5" - }, - - - { - "id": "edge-21", - "source": "node-11", - "target": "node-12" - }, - { - "id": "edge-22", - "source": "node-11", - "target": "node-13" - }, - { - "id": "edge-23", - "source": "node-11", - "target": "node-14" - }, - { - "id": "edge-24", - "source": "node-11", - "target": "node-15" - }, - { - "id": "edge-25", - "source": "node-12", - "target": "node-13" - }, - { - "id": "edge-26", - "source": "node-12", - "target": "node-14" - }, - { - "id": "edge-27", - "source": "node-12", - "target": "node-15" - }, - { - "id": "edge-28", - "source": "node-13", - "target": "node-14" - }, - { - "id": "edge-29", - "source": "node-13", - "target": "node-15" - }, - { - "id": "edge-30", - "source": "node-14", - "target": "node-15" - }, - { - "id": "edge-31", - "source": "node-0", - "target": "node-5" - }, - { - "id": "edge-32", - "source": "node-5", - "target": "node-11" - }, - { - "id": "edge-33", - "source": "node-11", - "target": "node-0" - }, - { - "id": "edge-34", - "source": "node-16", - "target": "node-0" - }, - { - "id": "edge-35", - "source": "node-16", - "target": "node-5" - }, - { - "id": "edge-36", - "source": "node-16", - "target": "node-11" - } - ] -} \ No newline at end of file diff --git a/packages/graph/tests/unit/data/test-data.ts b/packages/graph/tests/unit/data/test-data.ts deleted file mode 100644 index 8657282..0000000 --- a/packages/graph/tests/unit/data/test-data.ts +++ /dev/null @@ -1,38635 +0,0 @@ -export const nodes20 = {"nodes":[{"id":"5","dataType":"Person"},{"id":"4","dataType":"Person"},{"id":"6","dataType":"Person"},{"id":"7","dataType":"Person"},{"id":"1004","dataType":"Enterprise"},{"id":"1005","dataType":"Enterprise"},{"id":"8","dataType":"Person"},{"id":"1006","dataType":"Enterprise"},{"id":"9","dataType":"Person"},{"id":"1007","dataType":"Enterprise"},{"id":"10","dataType":"Person"},{"id":"1008","dataType":"Enterprise"},{"id":"11","dataType":"Person"},{"id":"1009","dataType":"Enterprise"},{"id":"2023","dataType":"Enterprise"},{"id":"1003","dataType":"Enterprise"},{"id":"2024","dataType":"Enterprise"},{"id":"2025","dataType":"Enterprise"},{"id":"2026","dataType":"Enterprise"},{"id":"2027","dataType":"Enterprise"}],"edges":[{"id":"edge-4|5|Person2Person#Social|1608187834","source":"4","target":"5","dataType":"Person2Person#Social"},{"id":"edge-4|6|Person2Person#Social|1608187834","source":"4","target":"6","dataType":"Person2Person#Social"},{"id":"edge-4|7|Person2Person#Social|1608187834","source":"4","target":"7","dataType":"Person2Person#Social"},{"id":"edge-4|5|Person2Person#Social|1609144894","source":"4","target":"5","dataType":"Person2Person#Social"},{"id":"edge-4|6|Person2Person#Social|1609144894","source":"4","target":"6","dataType":"Person2Person#Social"},{"id":"edge-4|7|Person2Person#Social|1609144894","source":"4","target":"7","dataType":"Person2Person#Social"},{"id":"edge-4|5|Person2Person#Social|1610530388","source":"4","target":"5","dataType":"Person2Person#Social"},{"id":"edge-4|6|Person2Person#Social|1610530388","source":"4","target":"6","dataType":"Person2Person#Social"},{"id":"edge-4|7|Person2Person#Social|1610530388","source":"4","target":"7","dataType":"Person2Person#Social"},{"id":"edge-4|5|Person2Person#Social|1612412936","source":"4","target":"5","dataType":"Person2Person#Social"},{"id":"edge-4|6|Person2Person#Social|1612412936","source":"4","target":"6","dataType":"Person2Person#Social"},{"id":"edge-4|7|Person2Person#Social|1612412936","source":"4","target":"7","dataType":"Person2Person#Social"},{"id":"edge-4|5|Person2Person#Benefit|1608187834","source":"4","target":"5","dataType":"Person2Person#Benefit"},{"id":"edge-4|6|Person2Person#Benefit|1608187834","source":"4","target":"6","dataType":"Person2Person#Benefit"},{"id":"edge-4|7|Person2Person#Benefit|1608187834","source":"4","target":"7","dataType":"Person2Person#Benefit"},{"id":"edge-4|5|Person2Person#Benefit|1609144894","source":"4","target":"5","dataType":"Person2Person#Benefit"},{"id":"edge-4|6|Person2Person#Benefit|1609144894","source":"4","target":"6","dataType":"Person2Person#Benefit"},{"id":"edge-4|7|Person2Person#Benefit|1609144894","source":"4","target":"7","dataType":"Person2Person#Benefit"},{"id":"edge-4|5|Person2Person#Benefit|1610530388","source":"4","target":"5","dataType":"Person2Person#Benefit"},{"id":"edge-4|6|Person2Person#Benefit|1610530388","source":"4","target":"6","dataType":"Person2Person#Benefit"},{"id":"edge-4|7|Person2Person#Benefit|1610530388","source":"4","target":"7","dataType":"Person2Person#Benefit"},{"id":"edge-4|5|Person2Person#Benefit|1612412936","source":"4","target":"5","dataType":"Person2Person#Benefit"},{"id":"edge-4|6|Person2Person#Benefit|1612412936","source":"4","target":"6","dataType":"Person2Person#Benefit"},{"id":"edge-4|7|Person2Person#Benefit|1612412936","source":"4","target":"7","dataType":"Person2Person#Benefit"},{"id":"edge-4|5|Person2Person#Co_Borrower|1608187834","source":"4","target":"5","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|6|Person2Person#Co_Borrower|1608187834","source":"4","target":"6","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|7|Person2Person#Co_Borrower|1608187834","source":"4","target":"7","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|5|Person2Person#Co_Borrower|1609144894","source":"4","target":"5","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|6|Person2Person#Co_Borrower|1609144894","source":"4","target":"6","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|7|Person2Person#Co_Borrower|1609144894","source":"4","target":"7","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|5|Person2Person#Co_Borrower|1610530388","source":"4","target":"5","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|6|Person2Person#Co_Borrower|1610530388","source":"4","target":"6","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|7|Person2Person#Co_Borrower|1610530388","source":"4","target":"7","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|5|Person2Person#Co_Borrower|1612412936","source":"4","target":"5","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|6|Person2Person#Co_Borrower|1612412936","source":"4","target":"6","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|7|Person2Person#Co_Borrower|1612412936","source":"4","target":"7","dataType":"Person2Person#Co_Borrower"},{"id":"edge-4|5|Person2Person#Guarantee|1608187834","source":"4","target":"5","dataType":"Person2Person#Guarantee"},{"id":"edge-4|6|Person2Person#Guarantee|1608187834","s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"source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1005|2026|Enterprise2Enterprise#Guarantee|1608187864","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1005|2025|Enterprise2Enterprise#Guarantee|1610530400","source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1005|2026|Enterprise2Enterprise#Guarantee|1610530400","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1005|2025|Enterprise2Enterprise#Guarantee|1612412941","source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1005|2026|Enterprise2Enterprise#Guarantee|1612412941","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1005|2025|Enterprise2Enterprise#Investment|1608187864","source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1005|2026|Enterprise2Enterprise#Investment|1608187864","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1005|2025|Enterprise2Enterprise#Investment|1610530400","source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1005|2026|Enterprise2Enterprise#Investment|1610530400","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1005|2025|Enterprise2Enterprise#Investment|1612412941","source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1005|2026|Enterprise2Enterprise#Investment|1612412941","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1005|2025|Enterprise2Enterprise#Level|1608187864","source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1005|2026|Enterprise2Enterprise#Level|1608187864","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1005|2025|Enterprise2Enterprise#Level|1610530400","source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1005|2026|Enterprise2Enterprise#Level|1610530400","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1005|2025|Enterprise2Enterprise#Level|1612412941","source":"1005","target":"2025","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1005|2026|Enterprise2Enterprise#Level|1612412941","source":"1005","target":"2026","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1006|2026|Enterprise2Enterprise#Guarantee|1608187864","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1006|2027|Enterprise2Enterprise#Guarantee|1608187864","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1006|2026|Enterprise2Enterprise#Guarantee|1610530400","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1006|2027|Enterprise2Enterprise#Guarantee|1610530400","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1006|2026|Enterprise2Enterprise#Guarantee|1612412941","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1006|2027|Enterprise2Enterprise#Guarantee|1612412941","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Guarantee"},{"id":"edge-1006|2026|Enterprise2Enterprise#Investment|1608187864","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1006|2027|Enterprise2Enterprise#Investment|1608187864","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1006|2026|Enterprise2Enterprise#Investment|1610530400","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1006|2027|Enterprise2Enterprise#Investment|1610530400","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1006|2026|Enterprise2Enterprise#Investment|1612412941","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1006|2027|Enterprise2Enterprise#Investment|1612412941","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Investment"},{"id":"edge-1006|2026|Enterprise2Enterprise#Level|1608187864","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1006|2027|Enterprise2Enterprise#Level|1608187864","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1006|2026|Enterprise2Enterprise#Level|1610530400","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1006|2027|Enterprise2Enterprise#Level|1610530400","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1006|2026|Enterprise2Enterprise#Level|1612412941","source":"1006","target":"2026","dataType":"Enterprise2Enterprise#Level"},{"id":"edge-1006|2027|Enterprise2Enterprise#Level|1612412941","source":"1006","target":"2027","dataType":"Enterprise2Enterprise#Level"}]} - -export const nodes77 = { - nodes: [ - { id: "0", cluster: "A", label: "0-A" }, - { id: "1", cluster: "B", label: "1-B" }, - { id: "2", cluster: "C", label: "2-C" }, - { id: "3", cluster: "C", label: "3-C" }, - { id: "4", cluster: "C", label: "4-C" }, - { id: "5", cluster: "B", label: "5-B" }, - { id: "6", cluster: "C", label: "6-C" }, - { id: "7", cluster: "B", label: "7-B" }, - { id: "8", cluster: "A", label: "8-A" }, - { id: "9", cluster: "A", label: "9-A" }, - { id: "10", cluster: "B", label: "10-B" }, - { id: "11", cluster: "B", label: "11-B" }, - { id: "12", cluster: "C", label: "12-C" }, - { id: "13", cluster: "B", label: "13-B" }, - { id: "14", cluster: "B", label: "14-B" }, - { id: "15", cluster: "B", label: "15-B" }, - { id: "16", cluster: "B", label: "16-B" }, - { id: "17", cluster: "B", label: "17-B" }, - { id: "18", cluster: "B", label: "18-B" }, - { id: "19", cluster: "C", label: "19-C" }, - { id: "20", cluster: "B", label: "20-B" }, - { id: "21", cluster: "A", label: "21-A" }, - { id: "22", cluster: "B", label: "22-B" }, - { id: "23", cluster: "B", label: "23-B" }, - { id: "24", cluster: "C", label: "24-C" }, - { id: "25", cluster: "C", label: "25-C" }, - { id: "26", cluster: "B", label: "26-B" }, - { id: "27", cluster: "A", label: "27-A" }, - { id: "28", cluster: "C", label: "28-C" }, - { id: "29", cluster: "C", label: "29-C" }, - { id: "30", cluster: "B", label: "30-B" }, - { id: "31", cluster: "B", label: "31-B" }, - { id: "32", cluster: "C", label: "32-C" }, - { id: "33", cluster: "A", label: "33-A" }, - { id: "34", cluster: "A", label: "34-A" }, - { id: "35", cluster: "C", label: "35-C" }, - { id: "36", cluster: "A", label: "36-A" }, - { id: "37", cluster: "C", label: "37-C" }, - { id: "38", cluster: "C", label: "38-C" }, - { id: "39", cluster: "B", label: "39-B" }, - { id: "40", cluster: "C", label: "40-C" }, - { id: "41", cluster: "B", label: "41-B" }, - { id: "42", cluster: "B", label: "42-B" }, - { id: "43", cluster: "A", label: "43-A" }, - { id: "44", cluster: "C", label: "44-C" }, - { id: "45", cluster: "B", label: "45-B" }, - { id: "46", cluster: "B", label: "46-B" }, - { id: "47", cluster: "C", label: "47-C" }, - { id: "48", cluster: "B", label: "48-B" }, - { id: "49", cluster: "C", label: "49-C" }, - { id: "50", cluster: "A", label: "50-A" }, - { id: "51", cluster: "C", label: "51-C" }, - { id: "52", cluster: "C", label: "52-C" }, - { id: "53", cluster: "C", label: "53-C" }, - { id: "54", cluster: "C", label: "54-C" }, - { id: "55", cluster: "A", label: "55-A" }, - { id: "56", cluster: "B", label: "56-B" }, - { id: "57", cluster: "A", label: "57-A" }, - { id: "58", cluster: "B", label: "58-B" }, - { id: "59", cluster: "C", label: "59-C" }, - { id: "60", cluster: "B", label: "60-B" }, - { id: "61", cluster: "B", label: "61-B" }, - { id: "62", cluster: "C", label: "62-C" }, - { id: "63", cluster: "B", label: "63-B" }, - { id: "64", cluster: "B", label: "64-B" }, - { id: "65", cluster: "A", label: "65-A" }, - { id: "66", cluster: "B", label: "66-B" }, - { id: "67", cluster: "C", label: "67-C" }, - { id: "68", cluster: "B", label: "68-B" }, - { id: "69", cluster: "A", label: "69-A" }, - { id: "70", cluster: "C", label: "70-C" }, - { id: "71", cluster: "C", label: "71-C" }, - { id: "72", cluster: "B", label: "72-B" }, - { id: "73", cluster: "A", label: "73-A" }, - { id: "74", cluster: "A", label: "74-A" }, - { id: "75", cluster: "A", label: "75-A" }, - { id: "76", cluster: "C", label: "76-C" }, - ], - edges: [ - { source: "1", target: "0", value: 1, cluster: "a", label: "a" }, - { source: "2", target: "0", value: 8, cluster: "c", label: "c" }, - { source: "3", target: "0", value: 10, cluster: "c", label: "c" }, - { source: "3", target: "2", value: 6, cluster: "c", label: "c" }, - { source: "4", target: "0", value: 1, cluster: "a", label: "a" }, - { source: "5", target: "0", value: 1, cluster: "a", label: "a" }, - { source: "6", target: "0", value: 1, cluster: "a", label: "a" }, - { source: "7", target: "0", value: 1, cluster: "a", label: "a" }, - { source: "8", target: "0", value: 2, cluster: "b", label: "b" }, - { source: "9", target: "0", value: 1, cluster: "a", label: "a" }, - { source: "11", target: "10", value: 1, cluster: "a", label: "a" }, - { source: "11", target: "3", value: 3, cluster: "b", label: "b" }, - { source: "11", target: "2", value: 3, cluster: "b", label: "b" }, - { source: "11", target: "0", value: 5, cluster: "c", label: "c" }, - { source: "12", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "13", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "14", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "15", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "17", target: "16", value: 4, cluster: "b", label: "b" }, - { source: "18", target: "16", value: 4, cluster: "b", label: "b" }, - { source: "18", target: "17", value: 4, cluster: "b", label: "b" }, - { source: "19", target: "16", value: 4, cluster: "b", label: "b" }, - { source: "19", target: "17", value: 4, cluster: "b", label: "b" }, - { source: "19", target: "18", value: 4, cluster: "b", label: "b" }, - { source: "20", target: "16", value: 3, cluster: "b", label: "b" }, - { source: "20", target: "17", value: 3, cluster: "b", label: "b" }, - { source: "20", target: "18", value: 3, cluster: "b", label: "b" }, - { source: "20", target: "19", value: 4, cluster: "b", label: "b" }, - { source: "21", target: "16", value: 3, cluster: "b", label: "b" }, - { source: "21", target: "17", value: 3, cluster: "b", label: "b" }, - { source: "21", target: "18", value: 3, cluster: "b", label: "b" }, - { source: "21", target: "19", value: 3, cluster: "b", label: "b" }, - { source: "21", target: "20", value: 5, cluster: "c", label: "c" }, - { source: "22", target: "16", value: 3, cluster: "b", label: "b" }, - { source: "22", target: "17", value: 3, cluster: "b", label: "b" }, - { source: "22", target: "18", value: 3, cluster: "b", label: "b" }, - { source: "22", target: "19", value: 3, cluster: "b", label: "b" }, - { source: "22", target: "20", value: 4, cluster: "b", label: "b" }, - { source: "22", target: "21", value: 4, cluster: "b", label: "b" }, - { source: "23", target: "16", value: 3, cluster: "b", label: "b" }, - { source: "23", target: "17", value: 3, cluster: "b", label: "b" }, - { source: "23", target: "18", value: 3, cluster: "b", label: "b" }, - { source: "23", target: "19", value: 3, cluster: "b", label: "b" }, - { source: "23", target: "20", value: 4, cluster: "b", label: "b" }, - { source: "23", target: "21", value: 4, cluster: "b", label: "b" }, - { source: "23", target: "22", value: 4, cluster: "b", label: "b" }, - { source: "23", target: "12", value: 2, cluster: "b", label: "b" }, - { source: "23", target: "11", value: 9, cluster: "c", label: "c" }, - { source: "24", target: "23", value: 2, cluster: "b", label: "b" }, - { source: "24", target: "11", value: 7, cluster: "c", label: "c" }, - { source: "25", target: "24", value: 13, cluster: "c", label: "c" }, - { source: "25", target: "23", value: 1, cluster: "a", label: "a" }, - { source: "25", target: "11", value: 12, cluster: "c", label: "c" }, - { source: "26", target: "24", value: 4, cluster: "b", label: "b" }, - { source: "26", target: "11", value: 31, cluster: "c", label: "c" }, - { source: "26", target: "16", value: 1, cluster: "a", label: "a" }, - { source: "26", target: "25", value: 1, cluster: "a", label: "a" }, - { source: "27", target: "11", value: 17, cluster: "c", label: "c" }, - { source: "27", target: "23", value: 5, cluster: "c", label: "c" }, - { source: "27", target: "25", value: 5, cluster: "c", label: "c" }, - { source: "27", target: "24", value: 1, cluster: "a", label: "a" }, - { source: "27", target: "26", value: 1, cluster: "a", label: "a" }, - { source: "28", target: "11", value: 8, cluster: "c", label: "c" }, - { source: "28", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "29", target: "23", value: 1, cluster: "a", label: "a" }, - { source: "29", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "29", target: "11", value: 2, cluster: "b", label: "b" }, - { source: "30", target: "23", value: 1, cluster: "a", label: "a" }, - { source: "31", target: "30", value: 2, cluster: "b", label: "b" }, - { source: "31", target: "11", value: 3, cluster: "b", label: "b" }, - { source: "31", target: "23", value: 2, cluster: "b", label: "b" }, - { source: "31", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "32", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "33", target: "11", value: 2, cluster: "b", label: "b" }, - { source: "33", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "34", target: "11", value: 3, cluster: "b", label: "b" }, - { source: "34", target: "29", value: 2, cluster: "b", label: "b" }, - { source: "35", target: "11", value: 3, cluster: "b", label: "b" }, - { source: "35", target: "34", value: 3, cluster: "b", label: "b" }, - { source: "35", target: "29", value: 2, cluster: "b", label: "b" }, - { source: "36", target: "34", value: 2, cluster: "b", label: "b" }, - { source: "36", target: "35", value: 2, cluster: "b", label: "b" }, - { source: "36", target: "11", value: 2, cluster: "b", label: "b" }, - { source: "36", target: "29", value: 1, cluster: "a", label: "a" }, - { source: "37", target: "34", value: 2, cluster: "b", label: "b" }, - { source: "37", target: "35", value: 2, cluster: "b", label: "b" }, - { source: "37", target: "36", value: 2, cluster: "b", label: "b" }, - { source: "37", target: "11", value: 2, cluster: "b", label: "b" }, - { source: "37", target: "29", value: 1, cluster: "a", label: "a" }, - { source: "38", target: "34", value: 2, cluster: "b", label: "b" }, - { source: "38", target: "35", value: 2, cluster: "b", label: "b" }, - { source: "38", target: "36", value: 2, cluster: "b", label: "b" }, - { source: "38", target: "37", value: 2, cluster: "b", label: "b" }, - { source: "38", target: "11", value: 2, cluster: "b", label: "b" }, - { source: "38", target: "29", value: 1, cluster: "a", label: "a" }, - { source: "39", target: "25", value: 1, cluster: "a", label: "a" }, - { source: "40", target: "25", value: 1, cluster: "a", label: "a" }, - { source: "41", target: "24", value: 2, cluster: "b", label: "b" }, - { source: "41", target: "25", value: 3, cluster: "b", label: "b" }, - { source: "42", target: "41", value: 2, cluster: "b", label: "b" }, - { source: "42", target: "25", value: 2, cluster: "b", label: "b" }, - { source: "42", target: "24", value: 1, cluster: "a", label: "a" }, - { source: "43", target: "11", value: 3, cluster: "b", label: "b" }, - { source: "43", target: "26", value: 1, cluster: "a", label: "a" }, - { source: "43", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "44", target: "28", value: 3, cluster: "b", label: "b" }, - { source: "44", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "45", target: "28", value: 2, cluster: "b", label: "b" }, - { source: "47", target: "46", value: 1, cluster: "a", label: "a" }, - { source: "48", target: "47", value: 2, cluster: "b", label: "b" }, - { source: "48", target: "25", value: 1, cluster: "a", label: "a" }, - { source: "48", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "48", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "49", target: "26", value: 3, cluster: "b", label: "b" }, - { source: "49", target: "11", value: 2, cluster: "b", label: "b" }, - { source: "50", target: "49", value: 1, cluster: "a", label: "a" }, - { source: "50", target: "24", value: 1, cluster: "a", label: "a" }, - { source: "51", target: "49", value: 9, cluster: "c", label: "c" }, - { source: "51", target: "26", value: 2, cluster: "b", label: "b" }, - { source: "51", target: "11", value: 2, cluster: "b", label: "b" }, - { source: "52", target: "51", value: 1, cluster: "a", label: "a" }, - { source: "52", target: "39", value: 1, cluster: "a", label: "a" }, - { source: "53", target: "51", value: 1, cluster: "a", label: "a" }, - { source: "54", target: "51", value: 2, cluster: "b", label: "b" }, - { source: "54", target: "49", value: 1, cluster: "a", label: "a" }, - { source: "54", target: "26", value: 1, cluster: "a", label: "a" }, - { source: "55", target: "51", value: 6, cluster: "c", label: "c" }, - { source: "55", target: "49", value: 12, cluster: "c", label: "c" }, - { source: "55", target: "39", value: 1, cluster: "a", label: "a" }, - { source: "55", target: "54", value: 1, cluster: "a", label: "a" }, - { source: "55", target: "26", value: 21, cluster: "c", label: "c" }, - { source: "55", target: "11", value: 19, cluster: "c", label: "c" }, - { source: "55", target: "16", value: 1, cluster: "a", label: "a" }, - { source: "55", target: "25", value: 2, cluster: "b", label: "b" }, - { source: "55", target: "41", value: 5, cluster: "c", label: "c" }, - { source: "55", target: "48", value: 4, cluster: "b", label: "b" }, - { source: "56", target: "49", value: 1, cluster: "a", label: "a" }, - { source: "56", target: "55", value: 1, cluster: "a", label: "a" }, - { source: "57", target: "55", value: 1, cluster: "a", label: "a" }, - { source: "57", target: "41", value: 1, cluster: "a", label: "a" }, - { source: "57", target: "48", value: 1, cluster: "a", label: "a" }, - { source: "58", target: "55", value: 7, cluster: "c", label: "c" }, - { source: "58", target: "48", value: 7, cluster: "c", label: "c" }, - { source: "58", target: "27", value: 6, cluster: "c", label: "c" }, - { source: "58", target: "57", value: 1, cluster: "a", label: "a" }, - { source: "58", target: "11", value: 4, cluster: "b", label: "b" }, - { source: "59", target: "58", value: 15, cluster: "c", label: "c" }, - { source: "59", target: "55", value: 5, cluster: "c", label: "c" }, - { source: "59", target: "48", value: 6, cluster: "c", label: "c" }, - { source: "59", target: "57", value: 2, cluster: "b", label: "b" }, - { source: "60", target: "48", value: 1, cluster: "a", label: "a" }, - { source: "60", target: "58", value: 4, cluster: "b", label: "b" }, - { source: "60", target: "59", value: 2, cluster: "b", label: "b" }, - { source: "61", target: "48", value: 2, cluster: "b", label: "b" }, - { source: "61", target: "58", value: 6, cluster: "c", label: "c" }, - { source: "61", target: "60", value: 2, cluster: "b", label: "b" }, - { source: "61", target: "59", value: 5, cluster: "c", label: "c" }, - { source: "61", target: "57", value: 1, cluster: "a", label: "a" }, - { source: "61", target: "55", value: 1, cluster: "a", label: "a" }, - { source: "62", target: "55", value: 9, cluster: "c", label: "c" }, - { source: "62", target: "58", value: 17, cluster: "c", label: "c" }, - { source: "62", target: "59", value: 13, cluster: "c", label: "c" }, - { source: "62", target: "48", value: 7, cluster: "c", label: "c" }, - { source: "62", target: "57", value: 2, cluster: "b", label: "b" }, - { source: "62", target: "41", value: 1, cluster: "a", label: "a" }, - { source: "62", target: "61", value: 6, cluster: "c", label: "c" }, - { source: "62", target: "60", value: 3, cluster: "b", label: "b" }, - { source: "63", target: "59", value: 5, cluster: "c", label: "c" }, - { source: "63", target: "48", value: 5, cluster: "c", label: "c" }, - { source: "63", target: "62", value: 6, cluster: "c", label: "c" }, - { source: "63", target: "57", value: 2, cluster: "b", label: "b" }, - { source: "63", target: "58", value: 4, cluster: "b", label: "b" }, - { source: "63", target: "61", value: 3, cluster: "b", label: "b" }, - { source: "63", target: "60", value: 2, cluster: "b", label: "b" }, - { source: "63", target: "55", value: 1, cluster: "a", label: "a" }, - { source: "64", target: "55", value: 5, cluster: "c", label: "c" }, - { source: "64", target: "62", value: 12, cluster: "c", label: "c" }, - { source: "64", target: "48", value: 5, cluster: "c", label: "c" }, - { source: "64", target: "63", value: 4, cluster: "b", label: "b" }, - { source: "64", target: "58", value: 10, cluster: "c", label: "c" }, - { source: "64", target: "61", value: 6, cluster: "c", label: "c" }, - { source: "64", target: "60", value: 2, cluster: "b", label: "b" }, - { source: "64", target: "59", value: 9, cluster: "c", label: "c" }, - { source: "64", target: "57", value: 1, cluster: "a", label: "a" }, - { source: "64", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "65", target: "63", value: 5, cluster: "c", label: "c" }, - { source: "65", target: "64", value: 7, cluster: "c", label: "c" }, - { source: "65", target: "48", value: 3, cluster: "b", label: "b" }, - { source: "65", target: "62", value: 5, cluster: "c", label: "c" }, - { source: "65", target: "58", value: 5, cluster: "c", label: "c" }, - { source: "65", target: "61", value: 5, cluster: "c", label: "c" }, - { source: "65", target: "60", value: 2, cluster: "b", label: "b" }, - { source: "65", target: "59", value: 5, cluster: "c", label: "c" }, - { source: "65", target: "57", value: 1, cluster: "a", label: "a" }, - { source: "65", target: "55", value: 2, cluster: "b", label: "b" }, - { source: "66", target: "64", value: 3, cluster: "b", label: "b" }, - { source: "66", target: "58", value: 3, cluster: "b", label: "b" }, - { source: "66", target: "59", value: 1, cluster: "a", label: "a" }, - { source: "66", target: "62", value: 2, cluster: "b", label: "b" }, - { source: "66", target: "65", value: 2, cluster: "b", label: "b" }, - { source: "66", target: "48", value: 1, cluster: "a", label: "a" }, - { source: "66", target: "63", value: 1, cluster: "a", label: "a" }, - { source: "66", target: "61", value: 1, cluster: "a", label: "a" }, - { source: "66", target: "60", value: 1, cluster: "a", label: "a" }, - { source: "67", target: "57", value: 3, cluster: "b", label: "b" }, - { source: "68", target: "25", value: 5, cluster: "c", label: "c" }, - { source: "68", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "68", target: "24", value: 1, cluster: "a", label: "a" }, - { source: "68", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "68", target: "48", value: 1, cluster: "a", label: "a" }, - { source: "68", target: "41", value: 1, cluster: "a", label: "a" }, - { source: "69", target: "25", value: 6, cluster: "c", label: "c" }, - { source: "69", target: "68", value: 6, cluster: "c", label: "c" }, - { source: "69", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "69", target: "24", value: 1, cluster: "a", label: "a" }, - { source: "69", target: "27", value: 2, cluster: "b", label: "b" }, - { source: "69", target: "48", value: 1, cluster: "a", label: "a" }, - { source: "69", target: "41", value: 1, cluster: "a", label: "a" }, - { source: "70", target: "25", value: 4, cluster: "b", label: "b" }, - { source: "70", target: "69", value: 4, cluster: "b", label: "b" }, - { source: "70", target: "68", value: 4, cluster: "b", label: "b" }, - { source: "70", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "70", target: "24", value: 1, cluster: "a", label: "a" }, - { source: "70", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "70", target: "41", value: 1, cluster: "a", label: "a" }, - { source: "70", target: "58", value: 1, cluster: "a", label: "a" }, - { source: "71", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "71", target: "69", value: 2, cluster: "b", label: "b" }, - { source: "71", target: "68", value: 2, cluster: "b", label: "b" }, - { source: "71", target: "70", value: 2, cluster: "b", label: "b" }, - { source: "71", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "71", target: "48", value: 1, cluster: "a", label: "a" }, - { source: "71", target: "41", value: 1, cluster: "a", label: "a" }, - { source: "71", target: "25", value: 1, cluster: "a", label: "a" }, - { source: "72", target: "26", value: 2, cluster: "b", label: "b" }, - { source: "72", target: "27", value: 1, cluster: "a", label: "a" }, - { source: "72", target: "11", value: 1, cluster: "a", label: "a" }, - { source: "73", target: "48", value: 2, cluster: "b", label: "b" }, - { source: "74", target: "48", value: 2, cluster: "b", label: "b" }, - { source: "74", target: "73", value: 3, cluster: "b", label: "b" }, - { source: "75", target: "69", value: 3, cluster: "b", label: "b" }, - { source: "75", target: "68", value: 3, cluster: "b", label: "b" }, - { source: "75", target: "25", value: 3, cluster: "b", label: "b" }, - { source: "75", target: "48", value: 1, cluster: "a", label: "a" }, - { source: "75", target: "41", value: 1, cluster: "a", label: "a" }, - { source: "75", target: "70", value: 1, cluster: "a", label: "a" }, - { source: "75", target: "71", value: 1, cluster: "a", label: "a" }, - { source: "76", target: "64", value: 1, cluster: "a", label: "a" }, - { source: "76", target: "65", value: 1, cluster: "a", label: "a" }, - { source: "76", target: "66", value: 1, cluster: "a", label: "a" }, - { source: "76", target: "63", value: 1, cluster: "a", label: "a" }, - { source: "76", target: "62", value: 1, cluster: "a", label: "a" }, - { source: "76", target: "48", value: 1, cluster: "a", label: "a" }, - { source: "76", target: "58", value: 1, cluster: "a", label: "a" }, - ], -}; - -export const nodes202 = { - nodes: [ - { - id: "1", - label: "1-A", - cluster: "A", - x: 301.20880899273163, - y: 16.498563471350245, - }, - { - id: "2", - label: "2-B", - cluster: "B", - x: 167.71579022730245, - y: 21.169092644111863, - }, - { - id: "3", - label: "3-C", - cluster: "C", - x: 225.60113755765104, - y: 53.38237357832761, - }, - { - id: "4", - label: "4-D", - cluster: "D", - x: 257.2171264302781, - y: 10.65803240773092, - }, - { - id: "5", - label: "5-E", - cluster: "E", - x: 631.9192989141275, - y: 16.185060075215628, - }, - { - id: "6", - label: "6-A", - cluster: "A", - x: 147.70380559678026, - y: 22.309088056991207, - }, - { - id: "7", - label: "7-B", - cluster: "B", - x: 127.84189789787449, - y: 93.67277180968523, - }, - { - id: "8", - label: "8-C", - cluster: "C", - x: 707.7325190040664, - y: 6.916731320922539, - }, - { - id: "9", - label: "9-D", - cluster: "D", - x: 235.0228391142811, - y: 4.721612635411866, - }, - { - id: "10", - label: "10-E", - cluster: "E", - x: 112.17360443437231, - y: 85.14322322856475, - }, - { - id: "11", - label: "11-A", - cluster: "A", - x: 151.94058879507244, - y: 39.805144202813125, - }, - { - id: "12", - label: "12-B", - cluster: "B", - x: 222.2986880590628, - y: 88.06947603346353, - }, - { - id: "13", - label: "13-C", - cluster: "C", - x: 604.6951590389173, - y: -53.95978614928713, - }, - { - id: "14", - label: "14-D", - cluster: "D", - x: 78.13869258561154, - y: 53.854113269521804, - }, - { - id: "15", - label: "15-E", - cluster: "E", - x: 57.32017518529654, - y: 104.76288631835605, - }, - { - id: "16", - label: "16-A", - cluster: "A", - x: 673.6741242664087, - y: 26.82550911323939, - }, - { - id: 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label: "a" }, - { id: "edge-11740", source: "94", target: "84", cluster: "c", label: "c" }, - { id: "edge-11760", source: "39", target: "48", cluster: "c", label: "c" }, - { id: "edge-11780", source: "98", target: "172", cluster: "c", label: "c" }, - { - id: "edge-11800", - source: "153", - target: "168", - cluster: "c", - label: "c", - }, - { id: "edge-11820", source: "80", target: "58", cluster: "a", label: "a" }, - { id: "edge-11840", source: "193", target: "58", cluster: "a", label: "a" }, - { id: "edge-11860", source: "55", target: "154", cluster: "c", label: "c" }, - { id: "edge-11880", source: "172", target: "22", cluster: "c", label: "c" }, - ], -}; - -export const nodes1589 = { - nodes: [ - { - id: "0", - olabel: "Kuperman_M", - x: 26.22602890442184, - y: 33.48766498594824, - cluster: "A", - label: "0-A", - degree: 1.4422495703074083, - }, - { - id: "1", - olabel: "Acebron_J", - x: 762.9747722969743, - y: 1076.5655474780835, - cluster: "B", - label: "1-B", - degree: 1.5874010519681994, - }, - { - id: "2", - olabel: "Bonilla_L", - x: 753.056692997006, - y: 1017.9822431435797, - cluster: "C", - label: "2-C", - degree: 1.5874010519681994, - }, - { - id: "3", - olabel: "Perezvicente_C", - x: 780.7209197522006, - y: 1043.2465155564882, - cluster: "A", - label: "3-A", - degree: 1.5874010519681994, - }, - { - id: "4", - olabel: "Ritort_F", - x: 720.7513914110218, - y: 1031.8931814456985, - cluster: "B", - label: "4-B", - degree: 1.5874010519681994, - }, - { - id: "5", - olabel: "Spigler_R", - x: 725.8939629672293, - y: 1070.7323716825538, - cluster: "C", - label: "5-C", - degree: 1.5874010519681994, - }, - { - id: "6", - olabel: "Adamic_L", - x: 232.80665735466172, - y: 1024.7862385984115, - cluster: "A", - label: "6-A", - degree: 1.5874010519681994, - }, - { - id: "7", - olabel: "Adar_E", - x: 282.21324055452686, - y: 1040.805009893429, - cluster: "B", - label: "7-B", - degree: 1, - }, - { - id: "8", - olabel: "Huberman_B", - x: 187.78093501739752, - y: 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"1561", - cluster: "A", - label: "A", - color: "#333", - id: "edge2699", - }, - { - source: "1565", - target: "1562", - cluster: "B", - label: "B", - color: "#333", - id: "edge2700", - }, - { - source: "1565", - target: "1563", - cluster: "C", - label: "C", - color: "#333", - id: "edge2701", - }, - { - source: "1565", - target: "1564", - cluster: "A", - label: "A", - color: "#333", - id: "edge2702", - }, - { - source: "1565", - target: "62", - cluster: "B", - label: "B", - color: "#333", - id: "edge2703", - }, - { - source: "1565", - target: "61", - cluster: "A", - label: "A", - color: "#333", - id: "edge2704", - }, - { - source: "1566", - target: "1561", - cluster: "B", - label: "B", - color: "#333", - id: "edge2705", - }, - { - source: "1566", - target: "1562", - cluster: "C", - label: "C", - color: "#333", - id: "edge2706", - }, - { - source: "1566", - target: "1563", - cluster: "A", - label: "A", - color: "#333", - id: "edge2707", - }, - { - source: "1566", - target: "1564", - cluster: "B", - label: "B", - color: "#333", - id: "edge2708", - }, - { - source: "1566", - target: "1565", - cluster: "C", - label: "C", - color: "#333", - id: "edge2709", - }, - { - source: "1566", - target: "62", - cluster: "C", - label: "C", - color: "#333", - id: "edge2710", - }, - { - source: "1566", - target: "61", - cluster: "B", - label: "B", - color: "#333", - id: "edge2711", - }, - { - source: "1568", - target: "1567", - cluster: "A", - label: "A", - color: "#333", - id: "edge2712", - }, - { - source: "1569", - target: "336", - cluster: "A", - label: "A", - color: "#333", - id: "edge2713", - }, - { - source: "1569", - target: "630", - cluster: "A", - label: "A", - color: "#333", - id: "edge2714", - }, - { - source: "1570", - target: "336", - cluster: "B", - label: "B", - color: "#333", - id: "edge2715", - }, - { - source: "1570", - target: "630", - cluster: "B", - label: "B", - color: "#333", - id: "edge2716", - }, - { - source: "1570", - target: "1569", - cluster: "B", - label: "B", - color: "#333", - id: "edge2717", - }, - { - source: "1571", - target: "336", - cluster: "C", - label: "C", - color: "#333", - id: "edge2718", - }, - { - source: "1571", - target: "630", - cluster: "C", - label: "C", - color: "#333", - id: "edge2719", - }, - { - source: "1571", - target: "1569", - cluster: "C", - label: "C", - color: "#333", - id: "edge2720", - }, - { - source: "1571", - target: "1570", - cluster: "A", - label: "A", - color: "#333", - id: "edge2721", - }, - { - source: "1572", - target: "336", - cluster: "A", - label: "A", - color: "#333", - id: "edge2722", - }, - { - source: "1572", - target: "630", - cluster: "A", - label: "A", - color: "#333", - id: "edge2723", - }, - { - source: "1572", - target: "1569", - cluster: "A", - label: "A", - color: "#333", - id: "edge2724", - }, - { - source: "1572", - target: "1570", - cluster: "B", - label: "B", - color: "#333", - id: "edge2725", - }, - { - source: "1572", - target: "1571", - cluster: "C", - label: "C", - color: "#333", - id: "edge2726", - }, - { - source: "1573", - target: "630", - cluster: "B", - label: "B", - color: "#333", - id: "edge2727", - }, - { - source: "1573", - target: "782", - cluster: "A", - label: "A", - color: "#333", - id: "edge2728", - }, - { - source: "1575", - target: "1574", - cluster: "C", - label: "C", - color: "#333", - id: "edge2729", - }, - { - source: "1576", - target: "1574", - cluster: "A", - label: "A", - color: "#333", - id: "edge2730", - }, - { - source: "1576", - target: "1575", - cluster: "B", - label: "B", - color: "#333", - id: "edge2731", - }, - { - source: "1577", - target: "1574", - cluster: "B", - label: "B", - color: "#333", - id: "edge2732", - }, - { - source: "1577", - target: "1575", - cluster: "C", - label: "C", - color: "#333", - id: "edge2733", - }, - { - source: "1577", - target: "1576", - cluster: "A", - label: "A", - color: "#333", - id: "edge2734", - }, - { - source: "1578", - target: "630", - cluster: "A", - label: "A", - color: "#333", - id: "edge2735", - }, - { - source: "1578", - target: "629", - cluster: "C", - label: "C", - color: "#333", - id: "edge2736", - }, - { - source: "1580", - target: "1579", - cluster: "A", - label: "A", - color: "#333", - id: "edge2737", - }, - { - source: "1583", - target: "1582", - cluster: "A", - label: "A", - color: "#333", - id: "edge2738", - }, - { - source: "1585", - target: "1584", - cluster: "B", - label: "B", - color: "#333", - id: "edge2739", - }, - { - source: "1586", - target: "1584", - cluster: "C", - label: "C", - color: "#333", - id: "edge2740", - }, - { - source: "1587", - target: "521", - cluster: "C", - label: "C", - color: "#333", - id: "edge2741", - }, - { - source: "1587", - target: "75", - cluster: "A", - label: "A", - color: "#333", - id: "edge2742", - }, - ], -}; diff --git a/packages/graph/tests/unit/degree-async-spec.ts b/packages/graph/tests/unit/degree-async-spec.ts deleted file mode 100644 index e9debc2..0000000 --- a/packages/graph/tests/unit/degree-async-spec.ts +++ /dev/null @@ -1,119 +0,0 @@ -import { getAlgorithm } from './utils'; - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - { - id: 'H', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'A', - target: 'C', - }, - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - { - source: 'G', - target: 'H', - }, - { - source: 'H', - target: 'G', - }, - ], -}; - -describe('degree async algorithm', () => { - it('getDegreeAsync', async () => { - const degree = { - A: { - degree: 3, - inDegree: 1, - outDegree: 2, - }, - B: { - degree: 2, - inDegree: 1, - outDegree: 1, - }, - C: { - degree: 2, - inDegree: 2, - outDegree: 0, - }, - D: { - degree: 3, - inDegree: 1, - outDegree: 2, - }, - E: { - degree: 2, - inDegree: 1, - outDegree: 1, - }, - F: { - degree: 2, - inDegree: 1, - outDegree: 1, - }, - G: { - degree: 2, - inDegree: 1, - outDegree: 1, - }, - H: { - degree: 2, - inDegree: 1, - outDegree: 1, - }, - }; - - const { getDegreeAsync } = await getAlgorithm(); - const result = await getDegreeAsync(data); - expect(result).toEqual(degree); - }); -}); diff --git a/packages/graph/tests/unit/degree-spec.ts b/packages/graph/tests/unit/degree-spec.ts deleted file mode 100644 index 3fedf65..0000000 --- a/packages/graph/tests/unit/degree-spec.ts +++ /dev/null @@ -1,139 +0,0 @@ -import { getDegree, getInDegree, getOutDegree } from '../../src' - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - { - id: 'H', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'A', - target: 'C', - }, - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - { - source: 'G', - target: 'H', - }, - { - source: 'H', - target: 'G', - }, - ], -}; - -describe('degree algorithm', () => { - it('getDegree', () => { - const degree = { - A: { - degree: 3, - inDegree: 1, - outDegree: 2 - }, - B: { - degree: 2, - inDegree: 1, - outDegree: 1 - }, - C: { - degree: 2, - inDegree: 2, - outDegree: 0 - }, - D: { - degree: 3, - inDegree: 1, - outDegree: 2 - }, - E: { - degree: 2, - inDegree: 1, - outDegree: 1 - }, - F: { - degree: 2, - inDegree: 1, - outDegree: 1 - }, - G: { - degree: 2, - inDegree: 1, - outDegree: 1 - }, - H: { - degree: 2, - inDegree: 1, - outDegree: 1 - } - } - let result = getDegree(data); - expect(result).toEqual(degree); - }); - - it('getInDegree', () => { - let result = getInDegree(data, 'A'); - expect(result).toBe(1); - - result = getInDegree(data, 'C') - expect(result).toBe(2) - - result = getInDegree(data, 'E') - expect(result).toBe(1) - }); - - it('getOutDegree', () => { - let result = getOutDegree(data, 'A'); - expect(result).toEqual(2); - - result = getOutDegree(data, 'D'); - expect(result).toEqual(2); - - result = getOutDegree(data, 'F'); - expect(result).toEqual(1); - }); -}); diff --git a/packages/graph/tests/unit/detect-cycle-async-spec.ts b/packages/graph/tests/unit/detect-cycle-async-spec.ts deleted file mode 100644 index f469fda..0000000 --- a/packages/graph/tests/unit/detect-cycle-async-spec.ts +++ /dev/null @@ -1,74 +0,0 @@ -import { getAlgorithm } from './utils'; - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'A', - target: 'C', - }, - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - ], -}; - -describe('(Async) detectDirectedCycle', () => { - it('should detect directed cycle', async () => { - const { detectCycleAsync } = await getAlgorithm(); - - let result = await detectCycleAsync(data); - expect(result).toBeNull(); - - data.edges.push({ - source: 'F', - target: 'D', - }); - - result = await detectCycleAsync(data); - expect(result).toEqual({ - D: 'F', - F: 'E', - E: 'D', - }); - }); -}); diff --git a/packages/graph/tests/unit/detect-cycle-spec.ts b/packages/graph/tests/unit/detect-cycle-spec.ts deleted file mode 100644 index 8bbabc2..0000000 --- a/packages/graph/tests/unit/detect-cycle-spec.ts +++ /dev/null @@ -1,508 +0,0 @@ -import detectDirectedCycle, { detectAllCycles } from '../../src/detect-cycle'; - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'A', - target: 'C', - }, - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - ], -}; - -describe('detectDirectedCycle', () => { - it('should detect directed cycle', () => { - let result = detectDirectedCycle(data); - // debugger - expect(result).toBeNull(); - - data.edges.push({ - source: 'F', - target: 'D', - }); - - // 返回格式： - // { currentNodeId: prevNode } - result = detectDirectedCycle(data); - expect(result).toEqual({ - D: 'F', - F: 'E', - E: 'D', - }); - }); - it('detect all cycles in directed graph', () => { - data.edges.push({ - source: 'C', - target: 'D', - }); - - const result = detectAllCycles(data, true); - expect(result.length).toEqual(3); - - const result2 = detectAllCycles(data, true, ['B']); - expect(result2.length).toEqual(1); - - expect(result2[0]).toEqual({ - A: { id: 'B' }, - B: { id: 'C' }, - C: { id: 'D' }, - D: { id: 'A' }, - }); - }); - it('detect cycle in undirected graph', () => { - const result = detectAllCycles(data); - expect(result.length).toEqual(3); - const result2 = detectAllCycles(data, false, ['B'], false); - expect(Object.keys(result2[0]).sort()).toEqual(['D', 'E', 'F']); - }); - it('test another graph', () => { - const graphData = { - nodes: [ - { - id: '0', - label: '0', - }, - { - id: '1', - label: '1', - }, - { - id: '2', - label: '2', - }, - { - id: '3', - label: '3', - }, - { - id: '4', - label: '4', - }, - { - id: '5', - label: '5', - }, - { - id: '6', - label: '6', - }, - { - id: '7', - label: '7', - }, - { - id: '8', - label: '8', - }, - { - id: '9', - label: '9', - }, - { - id: '10', - label: '10', - }, - { - id: '11', - label: '11', - }, - { - id: '12', - label: '12', - }, - { - id: '13', - label: '13', - }, - { - id: '14', - label: '14', - }, - { - id: '15', - label: '15', - }, - { - id: '16', - label: '16', - }, - { - id: '17', - label: '17', - }, - { - id: '18', - label: '18', - }, - { - id: '19', - label: '19', - }, - { - id: '20', - label: '20', - }, - { - id: '21', - label: '21', - }, - { - id: '22', - label: '22', - }, - { - id: '23', - label: '23', - }, - { - id: '24', - label: '24', - }, - { - id: '25', - label: '25', - }, - { - id: '26', - label: '26', - }, - { - id: '27', - label: '27', - }, - { - id: '28', - label: '28', - }, - { - id: '29', - label: '29', - }, - { - id: '30', - label: '30', - }, - { - id: '31', - label: '31', - }, - { - id: '32', - label: '32', - }, - { - id: '33', - label: '33', - }, - ], - edges: [ - { - source: '0', - target: '1', - }, - { - source: '0', - target: '2', - }, - { - source: '3', - target: '0', - }, - { - source: '0', - target: '4', - }, - { - source: '5', - target: '0', - }, - { - source: '0', - target: '7', - }, - { - source: '0', - target: '8', - }, - { - source: '0', - target: '9', - }, - { - source: '0', - target: '10', - }, - { - source: '0', - target: '11', - }, - { - source: '0', - target: '13', - }, - { - source: '14', - target: '0', - }, - { - source: '0', - target: '15', - }, - { - source: '0', - target: '16', - }, - { - source: '2', - target: '3', - }, - { - source: '4', - target: '5', - }, - { - source: '4', - target: '6', - }, - { - source: '5', - target: '6', - }, - { - source: '7', - target: '13', - }, - { - source: '8', - target: '14', - }, - { - source: '9', - target: '10', - }, - { - source: '10', - target: '22', - }, - { - source: '10', - target: '14', - }, - { - source: '10', - target: '12', - }, - { - source: '10', - target: '24', - }, - { - source: '10', - target: '21', - }, - { - source: '10', - target: '20', - }, - { - source: '11', - target: '24', - }, - { - source: '11', - target: '22', - }, - { - source: '11', - target: '14', - }, - { - source: '12', - target: '13', - }, - { - source: '16', - target: '17', - }, - { - source: '16', - target: '18', - }, - { - source: '16', - target: '21', - }, - { - source: '16', - target: '22', - }, - { - source: '17', - target: '18', - }, - { - source: '17', - target: '20', - }, - { - source: '18', - target: '19', - }, - { - source: '19', - target: '20', - }, - { - source: '19', - target: '33', - }, - { - source: '19', - target: '22', - }, - { - source: '19', - target: '23', - }, - { - source: '20', - target: '21', - }, - { - source: '21', - target: '22', - }, - { - source: '22', - target: '24', - }, - { - source: '22', - target: '25', - }, - { - source: '22', - target: '26', - }, - { - source: '22', - target: '23', - }, - { - source: '22', - target: '28', - }, - { - source: '22', - target: '30', - }, - { - source: '22', - target: '31', - }, - { - source: '22', - target: '32', - }, - { - source: '22', - target: '33', - }, - { - source: '23', - target: '28', - }, - { - source: '23', - target: '27', - }, - { - source: '23', - target: '29', - }, - { - source: '23', - target: '30', - }, - { - source: '23', - target: '31', - }, - { - source: '23', - target: '33', - }, - { - source: '32', - target: '33', - }, - ], - }; - const result = detectAllCycles(graphData, true, ['14']); - const result2 = detectAllCycles(graphData); - expect(result.length).toEqual(4); - expect(result2.length).toEqual(27); - }); - it('test a large graph', () => { - fetch('https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json') - .then((res) => res.json()) - .then((data) => { - data.nodes.forEach((node) => { - node.label = node.olabel; - node.degree = 0; - data.edges.forEach((edge) => { - if (edge.source === node.id || edge.target === node.id) { - node.degree++; - } - }); - }); - - const directedCycles = detectAllCycles(data, true); - expect(directedCycles.length).toEqual(0); - const undirectedCycles = detectAllCycles(data, false, ['1084'], false); - expect(undirectedCycles.length).toEqual(1548); - }); - }); -}); diff --git a/packages/graph/tests/unit/dfs-spec.ts b/packages/graph/tests/unit/dfs-spec.ts deleted file mode 100644 index 9afc547..0000000 --- a/packages/graph/tests/unit/dfs-spec.ts +++ /dev/null @@ -1,170 +0,0 @@ -import { depthFirstSearch } from '../../src'; - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'G', - }, - { - source: 'A', - target: 'D', - }, - { - source: 'A', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - { - source: 'D', - target: 'G', - }, - ], -}; - -describe('depthFirstSearch', () => { - it('should perform DFS operation on graph', () => { - - const enterNodeCallback = jest.fn(); - const leaveNodeCallback = jest.fn(); - - // Traverse graphs without callbacks first to check default ones. - depthFirstSearch(data, 'A'); - - // Traverse graph with enterNode and leaveNode callbacks. - depthFirstSearch(data, 'A', { - enter: enterNodeCallback, - leave: leaveNodeCallback, - }); - - expect(enterNodeCallback).toHaveBeenCalledTimes(data.nodes.length); - expect(leaveNodeCallback).toHaveBeenCalledTimes(data.nodes.length); - - const enterNodeParamsMap = [ - { currentNode: 'A', previousNode: '' }, - { currentNode: 'B', previousNode: 'A' }, - { currentNode: 'C', previousNode: 'B' }, - { currentNode: 'G', previousNode: 'C' }, - { currentNode: 'D', previousNode: 'A' }, - { currentNode: 'E', previousNode: 'A' }, - { currentNode: 'F', previousNode: 'E' }, - ]; - - for (let callIndex = 0; callIndex < data.nodes.length; callIndex += 1) { - const params = enterNodeCallback.mock.calls[callIndex][0]; - expect(params.current).toEqual(enterNodeParamsMap[callIndex].currentNode); - expect(params.previous).toEqual( - enterNodeParamsMap[callIndex].previousNode, - ); - } - - const leaveNodeParamsMap = [ - { currentNode: 'G', previousNode: 'C' }, - { currentNode: 'C', previousNode: 'B' }, - { currentNode: 'B', previousNode: 'A' }, - { currentNode: 'D', previousNode: 'A' }, - { currentNode: 'F', previousNode: 'E' }, - { currentNode: 'E', previousNode: 'A' }, - { currentNode: 'A', previousNode: '' }, - ]; - - for (let callIndex = 0; callIndex < data.nodes.length; callIndex += 1) { - const params = leaveNodeCallback.mock.calls[callIndex][0]; - expect(params.current).toEqual(leaveNodeParamsMap[callIndex].currentNode); - expect(params.previous).toEqual( - leaveNodeParamsMap[callIndex].previousNode, - ); - } - }); - - it('allow users to redefine node visiting logic', () => { - - const enterNodeCallback = jest.fn(); - const leaveNodeCallback = jest.fn(); - - depthFirstSearch(data, 'A', { - enter: enterNodeCallback, - leave: leaveNodeCallback, - allowTraversal: ({ current: currentNode, next: nextNode }) => { - return !(currentNode === 'A' && nextNode === 'B'); - }, - }); - - expect(enterNodeCallback).toHaveBeenCalledTimes(7); - expect(leaveNodeCallback).toHaveBeenCalledTimes(7); - - const enterNodeParamsMap = [ - { currentNode: 'A', previousNode: '' }, - { currentNode: 'D', previousNode: 'A' }, - { currentNode: 'G', previousNode: 'D' }, - { currentNode: 'E', previousNode: 'A' }, - { currentNode: 'F', previousNode: 'E' }, - { currentNode: 'D', previousNode: 'F' }, - { currentNode: 'G', previousNode: 'D' }, - ]; - - for (let callIndex = 0; callIndex < data.nodes.length; callIndex += 1) { - const params = enterNodeCallback.mock.calls[callIndex][0]; - expect(params.current).toEqual(enterNodeParamsMap[callIndex].currentNode); - expect(params.previous && params.previous).toEqual( - enterNodeParamsMap[callIndex].previousNode, - ); - } - - const leaveNodeParamsMap = [ - { currentNode: 'G', previousNode: 'D' }, - { currentNode: 'D', previousNode: 'A' }, - { currentNode: 'G', previousNode: 'D' }, - { currentNode: 'D', previousNode: 'F' }, - { currentNode: 'F', previousNode: 'E' }, - { currentNode: 'E', previousNode: 'A' }, - { currentNode: 'A', previousNode: '' }, - ]; - - for (let callIndex = 0; callIndex < data.nodes.length; callIndex += 1) { - const params = leaveNodeCallback.mock.calls[callIndex][0]; - expect(params.current).toEqual(leaveNodeParamsMap[callIndex].currentNode); - expect(params.previous).toEqual( - leaveNodeParamsMap[callIndex].previousNode, - ); - } - }); -}); diff --git a/packages/graph/tests/unit/find-path-async-spec.ts b/packages/graph/tests/unit/find-path-async-spec.ts deleted file mode 100644 index ef99e05..0000000 --- a/packages/graph/tests/unit/find-path-async-spec.ts +++ /dev/null @@ -1,132 +0,0 @@ -import { getAlgorithm } from './utils'; - -const data = { - nodes: [ - { - id: 'A', - label: 'A', - }, - { - id: 'B', - label: 'B', - }, - { - id: 'C', - label: 'C', - }, - { - id: 'D', - label: 'D', - }, - { - id: 'E', - label: 'E', - }, - { - id: 'F', - label: 'F', - }, - { - id: 'G', - label: 'G', - }, - { - id: 'H', - label: 'H', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'G', - }, - { - source: 'A', - target: 'D', - }, - { - source: 'A', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - { - source: 'D', - target: 'E', - }, - ], -}; - -describe('(Async) Shortest Path from source to target on graph', () => { - it('find the shortest path', async done => { - const { findShortestPathAsync } = await getAlgorithm(); - const { length, path } = await findShortestPathAsync(data, 'A', 'C'); - expect(length).toBe(2); - expect(path).toStrictEqual(['A', 'B', 'C']); - done(); - }); - - it('find all shortest paths', async done => { - const { findShortestPathAsync } = await getAlgorithm(); - const { length, allPath } = await findShortestPathAsync(data, 'A', 'F'); - expect(length).toBe(2); - expect(allPath[0]).toStrictEqual(['A', 'E', 'F']); - expect(allPath[1]).toStrictEqual(['A', 'D', 'F']); - - const { - length: directedLenght, - path: directedPath, - allPath: directedAllPath, - } = await findShortestPathAsync(data, 'A', 'F', true); - expect(directedLenght).toBe(2); - expect(directedAllPath[0]).toStrictEqual(['A', 'E', 'F']); - expect(directedPath).toStrictEqual(['A', 'E', 'F']); - done(); - }); - - it('find all paths', async done => { - const { findAllPathAsync } = await getAlgorithm(); - const allPath = await findAllPathAsync(data, 'A', 'E'); - expect(allPath.length).toBe(3); - expect(allPath[0]).toStrictEqual(['A', 'D', 'F', 'E']); - expect(allPath[1]).toStrictEqual(['A', 'D', 'E']); - expect(allPath[2]).toStrictEqual(['A', 'E']); - done(); - }); - - it('find all paths in directed graph', async done => { - const { findAllPathAsync } = await getAlgorithm(); - const allPath = await findAllPathAsync(data, 'A', 'E', true); - expect(allPath.length).toStrictEqual(2); - expect(allPath[0]).toStrictEqual(['A', 'D', 'E']); - expect(allPath[1]).toStrictEqual(['A', 'E']); - done(); - }); - - it('find all shortest paths in weighted graph', async done => { - const { findShortestPathAsync } = await getAlgorithm(); - data.edges.forEach((edge: any, i) => { - edge.weight = ((i % 2) + 1) * 2; - if (edge.source === 'F' && edge.target === 'D') edge.weight = 10; - }); - const { length, path, allPath } = await findShortestPathAsync(data, 'A', 'F', false, 'weight'); - expect(length).toBe(6); - expect(allPath[0]).toStrictEqual(['A', 'E', 'F']); - expect(path).toStrictEqual(['A', 'E', 'F']); - done(); - }); -}); diff --git a/packages/graph/tests/unit/floydWarshall-async-spec.ts b/packages/graph/tests/unit/floydWarshall-async-spec.ts deleted file mode 100644 index f33b939..0000000 --- a/packages/graph/tests/unit/floydWarshall-async-spec.ts +++ /dev/null @@ -1,110 +0,0 @@ -import { getAlgorithm } from './utils'; - -const data = { - nodes: [ - { - id: 'A', - label: '0', - }, - { - id: 'B', - label: '1', - }, - { - id: 'C', - label: '2', - }, - { - id: 'D', - label: '3', - }, - { - id: 'E', - label: '4', - }, - { - id: 'F', - label: '5', - }, - { - id: 'G', - label: '6', - }, - { - id: 'H', - label: '7', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'G', - }, - { - source: 'A', - target: 'D', - }, - { - source: 'A', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - ], -}; - -describe('(Async) Adjacency Matrix by Algorithm', () => { - it('get graph shortestpath matrix', async () => { - const { floydWarshallAsync } = await getAlgorithm(); - const matrix = await floydWarshallAsync(data); - expect(Object.keys(matrix).length).toBe(8); - const node0 = matrix[0]; - expect(node0.length).toBe(8); - expect(node0[0]).toBe(0); - expect(node0[1]).toBe(1); - expect(node0[2]).toBe(2); - expect(node0[3]).toBe(1); - expect(node0[4]).toBe(1); - expect(node0[5]).toBe(2); - expect(node0[6]).toBe(3); - expect(node0[7]).toBe(Infinity); - expect(matrix[1][7]).toBe(Infinity); - expect(matrix[2][7]).toBe(Infinity); - expect(matrix[3][7]).toBe(Infinity); - }); - - it('directed', async () => { - // directed - const { floydWarshallAsync } = await getAlgorithm(); - const matrix = await floydWarshallAsync(data, true); - expect(Object.keys(matrix).length).toBe(8); - const node0 = matrix[0]; - expect(node0.length).toBe(8); - expect(node0[0]).toBe(0); - expect(node0[1]).toBe(1); - expect(node0[2]).toBe(2); - expect(node0[3]).toBe(1); - expect(node0[4]).toBe(1); - expect(node0[5]).toBe(2); - expect(node0[6]).toBe(3); - expect(node0[7]).toBe(Infinity); - const node8 = matrix[6]; - expect(node8.length).toBe(8); - expect(node8[1]).toBe(Infinity); - expect(node8[6]).toBe(0); - }); -}); diff --git a/packages/graph/tests/unit/floydWarshall-spec.ts b/packages/graph/tests/unit/floydWarshall-spec.ts deleted file mode 100644 index 1fe4c44..0000000 --- a/packages/graph/tests/unit/floydWarshall-spec.ts +++ /dev/null @@ -1,108 +0,0 @@ -import { floydWarshall } from '../../src'; - -const data = { - nodes: [ - { - id: 'A', - label: '0', - }, - { - id: 'B', - label: '1', - }, - { - id: 'C', - label: '2', - }, - { - id: 'D', - label: '3', - }, - { - id: 'E', - label: '4', - }, - { - id: 'F', - label: '5', - }, - { - id: 'G', - label: '6', - }, - { - id: 'H', - label: '7', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'G', - }, - { - source: 'A', - target: 'D', - }, - { - source: 'A', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - ], -}; - -describe('Adjacency Matrix by Algorithm', () => { - it('get graph shortestpath matrix', () => { - const matrix = floydWarshall(data); - expect(Object.keys(matrix).length).toBe(8); - const node0 = matrix[0]; - expect(node0.length).toBe(8); - expect(node0[0]).toBe(0); - expect(node0[1]).toBe(1); - expect(node0[2]).toBe(2); - expect(node0[3]).toBe(1); - expect(node0[4]).toBe(1); - expect(node0[5]).toBe(2); - expect(node0[6]).toBe(3); - expect(node0[7]).toBe(Infinity); - expect(matrix[1][7]).toBe(Infinity); - expect(matrix[2][7]).toBe(Infinity); - expect(matrix[3][7]).toBe(Infinity); - }); - - it('directed', () => { - // directed - const matrix = floydWarshall(data, true); - expect(Object.keys(matrix).length).toBe(8); - const node0 = matrix[0]; - expect(node0.length).toBe(8); - expect(node0[0]).toBe(0); - expect(node0[1]).toBe(1); - expect(node0[2]).toBe(2); - expect(node0[3]).toBe(1); - expect(node0[4]).toBe(1); - expect(node0[5]).toBe(2); - expect(node0[6]).toBe(3); - expect(node0[7]).toBe(Infinity); - const node8 = matrix[6]; - expect(node8.length).toBe(8); - expect(node8[1]).toBe(Infinity); - expect(node8[6]).toBe(0); - }); -}); diff --git a/packages/graph/tests/unit/gSpan-spec.ts b/packages/graph/tests/unit/gSpan-spec.ts deleted file mode 100644 index c201a80..0000000 --- a/packages/graph/tests/unit/gSpan-spec.ts +++ /dev/null @@ -1,483 +0,0 @@ -import gSpan from "../../src/gSpan/gSpan"; - -const data1 = { - nodes: [ - { - id: "0", - cluster: "B", - }, - { - id: "1", - cluster: "B", - }, - { - id: "2", - cluster: "B", - }, - { - id: "3", - cluster: "C", - }, - { - id: "4", - cluster: "B", - }, - { - id: "5", - cluster: "D", - }, - { - id: "6", - cluster: "B", - }, - { - id: "7", - cluster: "C", - }, - { - id: "8", - cluster: "C", - }, - { - id: "9", - cluster: "C", - }, - { - id: "10", - cluster: "B", - }, - { - id: "11", - cluster: "B", - }, - { - id: "12", - cluster: "B", - }, - { - id: "13", - cluster: "B", - }, - { - id: "14", - cluster: "B", - }, - { - id: "15", - cluster: "B", - }, - { - id: "16", - cluster: "B", - }, - { - id: "17", - cluster: "B", - }, - { - id: "18", - cluster: "B", - }, - ], - edges: [ - { - source: "0", - target: "1", - cluster: "b", - }, - { - source: "0", - target: "2", - cluster: "b", - }, - { - source: "2", - target: "3", - cluster: "c", - }, - { - source: "2", - target: "4", - cluster: "b", - }, - { - source: "3", - target: "5", - cluster: "b", - }, - { - source: "4", - target: "6", - cluster: "b", - }, - { - source: "5", - target: "7", - cluster: "b", - }, - { - source: "5", - target: "8", - cluster: "b", - }, - { - source: "5", - target: "9", - cluster: "b", - }, - { - source: "6", - target: "10", - cluster: "b", - }, - { - source: "6", - target: "9", - cluster: "c", - }, - { - source: "7", - target: "11", - cluster: "c", - }, - { - source: "8", - target: "12", - cluster: "c", - }, - { - source: "10", - target: "13", - cluster: "b", - }, - { - source: "11", - target: "14", - cluster: "b", - }, - { - source: "11", - target: "15", - cluster: "b", - }, - { - source: "12", - target: "16", - cluster: "b", - }, - { - source: "12", - target: "15", - cluster: "b", - }, - { - source: "14", - target: "17", - cluster: "b", - }, - { - source: "16", - target: "18", - cluster: "b", - }, - ], -}; -const data2 = { - nodes: [ - { - id: "0", - cluster: "B", - }, - { - id: "1", - cluster: "B", - }, - { - id: "2", - cluster: "F", - }, - { - id: "3", - cluster: "F", - }, - { - id: "4", - cluster: "C", - }, - { - id: "5", - cluster: "C", - }, - { - id: "6", - cluster: "C", - }, - { - id: "7", - cluster: "C", - }, - { - id: "8", - cluster: "C", - }, - { - id: "9", - cluster: "C", - }, - ], - edges: [ - { - source: "0", - target: "1", - cluster: "b", - }, - { - source: "0", - target: "2", - cluster: "b", - }, - { - source: "1", - target: "3", - cluster: "b", - }, - { - source: "2", - target: "4", - cluster: "c", - }, - { - source: "2", - target: "5", - cluster: "c", - }, - { - source: "2", - target: "6", - cluster: "b", - }, - { - source: "3", - target: "7", - cluster: "c", - }, - { - source: "3", - target: "8", - cluster: "c", - }, - { - source: "3", - target: "9", - cluster: "b", - }, - ], -}; -const data3 = { - nodes: [ - { - id: "0", - cluster: "F", - }, - { - id: "1", - cluster: "F", - }, - { - id: "2", - cluster: "B", - }, - { - id: "3", - cluster: "B", - }, - { - id: "4", - cluster: "B", - }, - { - id: "5", - cluster: "B", - }, - { - id: "6", - cluster: "B", - }, - { - id: "7", - cluster: "B", - }, - { - id: "8", - cluster: "B", - }, - { - id: "9", - cluster: "B", - }, - { - id: "10", - cluster: "B", - }, - { - id: "11", - cluster: "B", - }, - { - id: "12", - cluster: "B", - }, - { - id: "13", - cluster: "B", - }, - { - id: "14", - cluster: "C", - }, - { - id: "15", - cluster: "C", - }, - { - id: "16", - cluster: "B", - }, - { - id: "17", - cluster: "C", - }, - { - id: "18", - cluster: "C", - }, - { - id: "19", - cluster: "B", - }, - ], - edges: [ - { - source: "0", - target: "1", - cluster: "b", - }, - { - source: "0", - target: "2", - cluster: "b", - }, - { - source: "1", - target: "3", - cluster: "b", - }, - { - source: "2", - target: "4", - cluster: "e", - }, - { - source: "2", - target: "5", - cluster: "e", - }, - { - source: "3", - target: "6", - cluster: "e", - }, - { - source: "3", - target: "7", - cluster: "e", - }, - { - source: "4", - target: "2", - cluster: "b", - }, - { - source: "4", - target: "9", - cluster: "e", - }, - { - source: "5", - target: "10", - cluster: "e", - }, - { - source: "6", - target: "11", - cluster: "b", - }, - { - source: "6", - target: "12", - cluster: "e", - }, - { - source: "7", - target: "13", - cluster: "e", - }, - { - source: "8", - target: "14", - cluster: "c", - }, - { - source: "8", - target: "15", - cluster: "b", - }, - { - source: "9", - target: "16", - cluster: "e", - }, - { - source: "10", - target: "16", - cluster: "e", - }, - { - source: "11", - target: "17", - cluster: "c", - }, - { - source: "11", - target: "18", - cluster: "b", - }, - { - source: "12", - target: "19", - cluster: "e", - }, - { - source: "13", - target: "19", - cluster: "e", - }, - ], -}; - -describe("gSpan", () => { - it("gSpan first test", () => { - const graphDataMap = { - "a-name": data1, - "b-name": data2, - "c-name": data3, - }; - const result = gSpan({ - graphs: graphDataMap, - minSupport: 3, - minNodeNum: 2, - maxNodeNum: 4, - }); - console.log(result); - }); -}); diff --git a/packages/graph/tests/unit/gaddi-async-spec.ts b/packages/graph/tests/unit/gaddi-async-spec.ts deleted file mode 100644 index 2d65c9b..0000000 --- a/packages/graph/tests/unit/gaddi-async-spec.ts +++ /dev/null @@ -1,388 +0,0 @@ -import { getAlgorithm } from './utils'; -import { nodes77, nodes202 } from './data/test-data'; - -const data3 = { - nodes: [ - { - id: '0', - cluster: 'F', - }, - { - id: '1', - cluster: 'F', - }, - { - id: '2', - cluster: 'B', - }, - { - id: '3', - cluster: 'B', - }, - { - id: '4', - cluster: 'B', - }, - { - id: '5', - cluster: 'B', - }, - { - id: '6', - cluster: 'B', - }, - { - id: '7', - cluster: 'B', - }, - { - id: '8', - cluster: 'B', - }, - { - id: '9', - cluster: 'B', - }, - { - id: '10', - cluster: 'B', - }, - { - id: '11', - cluster: 'B', - }, - { - id: '12', - cluster: 'B', - }, - { - id: '13', - cluster: 'B', - }, - { - id: '14', - cluster: 'C', - }, - { - id: '15', - cluster: 'C', - }, - { - id: '16', - cluster: 'B', - }, - { - id: '17', - cluster: 'C', - }, - { - id: '18', - cluster: 'C', - }, - { - id: '19', - cluster: 'B', - }, - ], - edges: [ - { - source: '0', - target: '1', - cluster: 'b', - }, - { - source: '0', - target: '2', - cluster: 'b', - }, - { - source: '1', - target: '3', - cluster: 'b', - }, - { - source: '2', - target: '4', - cluster: 'e', - }, - { - source: '2', - target: '5', - cluster: 'e', - }, - { - source: '3', - target: '6', - cluster: 'e', - }, - { - source: '3', - target: '7', - cluster: 'e', - }, - { - source: '4', - target: '2', - cluster: 'b', - }, - { - source: '4', - target: '9', - cluster: 'e', - }, - { - source: '5', - target: '10', - cluster: 'e', - }, - { - source: '6', - target: '11', - cluster: 'b', - }, - { - source: '6', - target: '12', - cluster: 'e', - }, - { - source: '7', - target: '13', - cluster: 'e', - }, - { - source: '8', - target: '14', - cluster: 'c', - }, - { - source: '8', - target: '15', - cluster: 'b', - }, - { - source: '9', - target: '16', - cluster: 'e', - }, - { - source: '10', - target: '16', - cluster: 'e', - }, - { - source: '11', - target: '17', - cluster: 'c', - }, - { - source: '11', - target: '18', - cluster: 'c', - }, - { - source: '12', - target: '19', - cluster: 'e', - }, - { - source: '13', - target: '19', - cluster: 'e', - }, - ], -}; - -const pattern1 = { - nodes: [ - { - id: '0', - cluster: 'F', - }, - { - id: '1', - cluster: 'F', - }, - { - id: '2', - cluster: 'B', - }, - ], - edges: [ - { - source: '0', - target: '1', - cluster: 'b', - }, - { - source: '0', - target: '2', - cluster: 'b', - }, - ], -}; - -describe('(Async) gSpan', () => { - it('gSpan match pattern 1', async () => { - const { GADDIAsync } = await getAlgorithm(); - const matchedSubGraphs = await GADDIAsync( - data3, - pattern1, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - expect(matchedSubGraphs.length).toBe(2); - matchedSubGraphs.forEach(graph => { - expect(graph.nodes[0].cluster).toBe('F'); - expect(graph.nodes[1].cluster).toBe('F'); - expect(graph.nodes[2].cluster).toBe('B'); - expect(graph.edges[0].cluster).toBe('b'); - expect(graph.edges[1].cluster).toBe('b'); - }); - }); -}); - -describe('(Async) Performance: gSpan 77 nodes G', () => { - it('pattern 10 nodes', async () => { - const patternWith10Nodes = { - nodes: [ - { id: 'pn1', cluster: 'B' }, - { id: 'pn2', cluster: 'B' }, - { id: 'pn3', cluster: 'B' }, - { id: 'pn4', cluster: 'C' }, - { id: 'pn5', cluster: 'B' }, - { id: 'pn6', cluster: 'A' }, - { id: 'pn7', cluster: 'B' }, - { id: 'pn8', cluster: 'B' }, - { id: 'pn9', cluster: 'A' }, - { id: 'pn10', cluster: 'B' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'b' }, - { source: 'pn1', target: 'pn3', cluster: 'b' }, - { source: 'pn1', target: 'pn4', cluster: 'b' }, - { source: 'pn1', target: 'pn5', cluster: 'b' }, - { source: 'pn1', target: 'pn6', cluster: 'b' }, - { source: 'pn1', target: 'pn7', cluster: 'b' }, - { source: 'pn1', target: 'pn8', cluster: 'b' }, - - { source: 'pn2', target: 'pn3', cluster: 'b' }, - { source: 'pn2', target: 'pn4', cluster: 'b' }, - { source: 'pn2', target: 'pn5', cluster: 'b' }, - { source: 'pn2', target: 'pn6', cluster: 'b' }, - { source: 'pn2', target: 'pn7', cluster: 'b' }, - { source: 'pn2', target: 'pn8', cluster: 'b' }, - - { source: 'pn3', target: 'pn4', cluster: 'b' }, - { source: 'pn3', target: 'pn5', cluster: 'b' }, - { source: 'pn3', target: 'pn6', cluster: 'b' }, - { source: 'pn3', target: 'pn7', cluster: 'b' }, - { source: 'pn3', target: 'pn8', cluster: 'b' }, - - { source: 'pn4', target: 'pn5', cluster: 'b' }, - { source: 'pn4', target: 'pn6', cluster: 'b' }, - { source: 'pn4', target: 'pn7', cluster: 'b' }, - { source: 'pn4', target: 'pn8', cluster: 'b' }, - - { source: 'pn5', target: 'pn6', cluster: 'b' }, - { source: 'pn5', target: 'pn7', cluster: 'b' }, - { source: 'pn5', target: 'pn8', cluster: 'b' }, - - { source: 'pn6', target: 'pn7', cluster: 'b' }, - { source: 'pn6', target: 'pn8', cluster: 'b' }, - - { source: 'pn7', target: 'pn8', cluster: 'b' }, - - { source: 'pn8', target: 'pn9', cluster: 'a' }, - { source: 'pn8', target: 'pn10', cluster: 'a' }, - ], - }; - const begin = performance.now(); - const { GADDIAsync } = await getAlgorithm(); - const result = await GADDIAsync( - nodes77, - patternWith10Nodes, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log( - '77 nodes graph matching 10 nodes pattern', - performance.now() - begin, - result.length, - ); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - expect(result.length).toBe(3); - - expect(result[0].nodes[0].id).toBe('16'); - expect(result[1].nodes[0].id).toBe('17'); - expect(result[2].nodes[0].id).toBe('23'); - }); -}); - -describe('(Async) Performance: 202 nodes G', () => { - it('pattern with 14 nodes, directed', async () => { - const patternWith14Nodes = { - nodes: [ - { id: 'pn1', cluster: 'D' }, - { id: 'pn2', cluster: 'C' }, - { id: 'pn3', cluster: 'D' }, - { id: 'pn4', cluster: 'D' }, - { id: 'pn5', cluster: 'C' }, - { id: 'pn6', cluster: 'B' }, - { id: 'pn7', cluster: 'E' }, - { id: 'pn8', cluster: 'C' }, - { id: 'pn9', cluster: 'C' }, - { id: 'pn10', cluster: 'B' }, - { id: 'pn11', cluster: 'B' }, - { id: 'pn12', cluster: 'A' }, - { id: 'pn13', cluster: 'E' }, - { id: 'pn14', cluster: 'B' }, - ], - edges: [ - { source: 'pn2', target: 'pn1', cluster: 'c' }, - { source: 'pn3', target: 'pn1', cluster: 'a' }, - { source: 'pn4', target: 'pn1', cluster: 'c' }, - { source: 'pn1', target: 'pn5', cluster: 'c' }, - { source: 'pn6', target: 'pn1', cluster: 'c' }, - { source: 'pn7', target: 'pn1', cluster: 'c' }, - { source: 'pn8', target: 'pn1', cluster: 'c' }, - { source: 'pn1', target: 'pn9', cluster: 'a' }, - { source: 'pn10', target: 'pn1', cluster: 'c' }, - { source: 'pn11', target: 'pn1', cluster: 'a' }, - { source: 'pn1', target: 'pn12', cluster: 'c' }, - { source: 'pn1', target: 'pn13', cluster: 'c' }, - { source: 'pn14', target: 'pn13', cluster: 'c' }, - ], - }; - const begin = performance.now(); - const { GADDIAsync } = await getAlgorithm(); - const result = await GADDIAsync( - nodes202, - patternWith14Nodes, - true, - undefined, - undefined, - 'cluster', - 'cluster', - ); - - console.log( - '202 nodes graph matching 14 nodes pattern, directed', - performance.now() - begin, - result.length, - ); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - expect(result.length).toBe(1); - expect(result[0].nodes[0].id).toBe('49'); - }); -}); diff --git a/packages/graph/tests/unit/gaddi-spec.ts b/packages/graph/tests/unit/gaddi-spec.ts deleted file mode 100644 index 03f2383..0000000 --- a/packages/graph/tests/unit/gaddi-spec.ts +++ /dev/null @@ -1,1110 +0,0 @@ -import GADDI from '../../src/gaddi'; -import { nodes77, nodes202, nodes1589, nodes20 } from './data/test-data'; - -const data3 = { - nodes: [ - { - id: '0', - cluster: 'F', - }, - { - id: '1', - cluster: 'F', - }, - { - id: '2', - cluster: 'B', - }, - { - id: '3', - cluster: 'B', - }, - { - id: '4', - cluster: 'B', - }, - { - id: '5', - cluster: 'B', - }, - { - id: '6', - cluster: 'B', - }, - { - id: '7', - cluster: 'B', - }, - { - id: '8', - cluster: 'B', - }, - { - id: '9', - cluster: 'B', - }, - { - id: '10', - cluster: 'B', - }, - { - id: '11', - cluster: 'B', - }, - { - id: '12', - cluster: 'B', - }, - { - id: '13', - cluster: 'B', - }, - { - id: '14', - cluster: 'C', - }, - { - id: '15', - cluster: 'C', - }, - { - id: '16', - cluster: 'B', - }, - { - id: '17', - cluster: 'C', - }, - { - id: '18', - cluster: 'C', - }, - { - id: '19', - cluster: 'B', - }, - ], - edges: [ - { - source: '0', - target: '1', - cluster: 'b', - }, - { - source: '0', - target: '2', - cluster: 'b', - }, - { - source: '1', - target: '3', - cluster: 'b', - }, - { - source: '2', - target: '4', - cluster: 'e', - }, - { - source: '2', - target: '5', - cluster: 'e', - }, - { - source: '3', - target: '6', - cluster: 'e', - }, - { - source: '3', - target: '7', - cluster: 'e', - }, - { - source: '4', - target: '2', - cluster: 'b', - }, - { - source: '4', - target: '9', - cluster: 'e', - }, - { - source: '5', - target: '10', - cluster: 'e', - }, - { - source: '6', - target: '11', - cluster: 'b', - }, - { - source: '6', - target: '12', - cluster: 'e', - }, - { - source: '7', - target: '13', - cluster: 'e', - }, - { - source: '8', - target: '14', - cluster: 'c', - }, - { - source: '8', - target: '15', - cluster: 'b', - }, - { - source: '9', - target: '16', - cluster: 'e', - }, - { - source: '10', - target: '16', - cluster: 'e', - }, - { - source: '11', - target: '17', - cluster: 'c', - }, - { - source: '11', - target: '18', - cluster: 'c', - }, - { - source: '12', - target: '19', - cluster: 'e', - }, - { - source: '13', - target: '19', - cluster: 'e', - }, - ], -}; - -const pattern1 = { - nodes: [ - { - id: '0', - cluster: 'F', - }, - { - id: '1', - cluster: 'F', - }, - { - id: '2', - cluster: 'B', - }, - ], - edges: [ - { - source: '0', - target: '1', - cluster: 'b', - }, - { - source: '0', - target: '2', - cluster: 'b', - }, - ], -}; - -const pattern2 = { - nodes: [ - { - id: '11', - cluster: 'B', - }, - { - id: '17', - cluster: 'C', - }, - { - id: '18', - cluster: 'C', - }, - ], - edges: [ - { - source: '11', - target: '17', - cluster: 'c', - }, - { - source: '11', - target: '18', - cluster: 'b', - }, - ], -}; - -const circlePattern = { - nodes: [ - { id: '1', cluster: 'B' }, - { id: '2', cluster: 'B' }, - { id: '3', cluster: 'B' }, - { id: '4', cluster: 'B' }, - { id: '5', cluster: 'B' }, - { id: '6', cluster: 'B' }, - ], - edges: [ - { source: '1', target: '2', cluster: 'e' }, - { source: '2', target: '3', cluster: 'e' }, - { source: '3', target: '4', cluster: 'e' }, - { source: '4', target: '5', cluster: 'e' }, - { source: '5', target: '6', cluster: 'e' }, - { source: '6', target: '1', cluster: 'e' }, - ], -}; - -describe('gSpan', () => { - it('gSpan match pattern 1', () => { - const matchedSubGraphs = GADDI( - data3, - pattern1, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log('test1', matchedSubGraphs); - expect(matchedSubGraphs.length).toBe(2); - matchedSubGraphs.forEach(graph => { - expect(graph.nodes[0].cluster).toBe('B'); - expect(graph.nodes[1].cluster).toBe('F'); - expect(graph.nodes[2].cluster).toBe('F'); - expect(graph.edges[0].cluster).toBe('b'); - expect(graph.edges[1].cluster).toBe('b'); - }); - }); - it('gSpan match pattern 2', () => { - const matchedSubGraphs = GADDI(data3, pattern2, false, 2, 1, 'cluster', 'cluster'); - console.log('test2', matchedSubGraphs); - expect(matchedSubGraphs.length).toBe(1); - // expect(matchedSubGraphs.nodes) - }); - it('gSpan match circular', () => { - const matchedSubGraphs = GADDI( - data3, - circlePattern, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - expect(matchedSubGraphs.length).toBe(2); - matchedSubGraphs.forEach(graph => { - graph.nodes.forEach(node => { - expect(node.cluster).toBe('B'); - }); - graph.edges.forEach(edge => { - expect(edge.cluster).toBe('e'); - }); - }); - }); - it('gSpan match circular2 with a parallel edge', () => { - const circlePattern2 = { - nodes: [ - { id: '1', cluster: 'B' }, - { id: '2', cluster: 'B' }, - { id: '3', cluster: 'B' }, - { id: '4', cluster: 'B' }, - { id: '5', cluster: 'B' }, - { id: '6', cluster: 'B' }, - ], - edges: [ - { source: '1', target: '2', cluster: 'e' }, - { source: '2', target: '3', cluster: 'e' }, - { source: '3', target: '4', cluster: 'e' }, - { source: '4', target: '5', cluster: 'e' }, // 平行边 - { source: '4', target: '5', cluster: 'b' }, // 平行边 - { source: '5', target: '6', cluster: 'e' }, - { source: '6', target: '1', cluster: 'e' }, - ], - }; - const matchedSubGraphs = GADDI( - data3, - circlePattern2, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log('circle 2', matchedSubGraphs); - expect(matchedSubGraphs.length).toBe(1); - - matchedSubGraphs[0].nodes.forEach(node => { - expect(node.cluster).toBe('B'); - }); - matchedSubGraphs[0].edges.forEach((edge, i) => { - if (i === 2) { - expect(edge.cluster).toBe('b'); - return; - } - expect(edge.cluster).toBe('e'); - }); - }); - // 平行边可能被匹配成多条单独边 - it('gSpan match two parallel edges', () => { - const pattern3 = { - nodes: [ - { id: 'node1', cluster: 'B' }, - { id: 'node2', cluster: 'B' }, - ], - edges: [ - { source: 'node1', target: 'node2', cluster: 'b' }, - { source: 'node1', target: 'node2', cluster: 'e' }, - ], - }; - const matchedSubGraphs = GADDI( - data3, - pattern3, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log('test3', matchedSubGraphs); - // expect(matchedSubGraphs.length).toBe(2); - // matchedSubGraphs.forEach((graph) => { - // expect(graph.nodes[0].cluster).toBe("F"); - // expect(graph.nodes[1].cluster).toBe("F"); - // expect(graph.nodes[2].cluster).toBe("B"); - // expect(graph.edges[0].cluster).toBe("b"); - // expect(graph.edges[1].cluster).toBe("b"); - // }); - }); -}); - -describe('gSpan directed', () => { - it('gSpan match pattern 1', () => { - const pattern11 = { - nodes: [ - { - id: '0', - cluster: 'F', - }, - { - id: '1', - cluster: 'F', - }, - { - id: '2', - cluster: 'B', - }, - ], - edges: [ - { - source: '0', - target: '1', - cluster: 'b', - }, - { - source: '0', - target: '2', - cluster: 'b', - }, - ], - }; - const matchedSubGraphs = GADDI( - data3, - pattern11, - true, - undefined, - undefined, - 'cluster', - 'cluster', - ); - expect(matchedSubGraphs.length).toBe(2); - expect(matchedSubGraphs[0].nodes[0].id).toBe('2'); - expect(matchedSubGraphs[0].nodes[1].id).toBe('0'); - expect(matchedSubGraphs[0].nodes[2].id).toBe('1'); - }); -}); - -describe('GADDI switch nodes', () => { - it('gSpan match pattern 1', () => { - const pattern1 = { - nodes: [ - { id: 'Person', dataType: 'Person' }, - { id: 'Enterprise', dataType: 'Enterprise' }, - ], - edges: [ - { - id: 'edge-1613700998017', - source: 'Person', - target: 'Enterprise', - dataType: 'Person2Enterprise#Guarantee', - rules: [], - }, - ], - }; - const res1 = GADDI(nodes20, pattern1, true, undefined, undefined, 'dataType', 'dataType'); - expect(res1.length).toBe(6); - const pattern2 = { - nodes: [ - { id: 'Enterprise', dataType: 'Enterprise' }, - { id: 'Person', dataType: 'Person' }, - ], - edges: [ - { - id: 'edge-1613700998017', - source: 'Person', - target: 'Enterprise', - dataType: 'Person2Enterprise#Guarantee', - rules: [], - }, - ], - }; - const res2 = GADDI(nodes20, pattern2, true, undefined, undefined, 'dataType', 'dataType'); - expect(res2.length).toBe(6); - }); -}); - -describe('Performance: gSpan 77 nodes G', () => { - // 100ms - it('pattern 3 nodes', () => { - const patternWith3Nodes = { - nodes: [ - { id: 'pn1', cluster: 'A' }, - { id: 'pn2', cluster: 'B' }, - { id: 'pn3', cluster: 'A' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'b' }, - { source: 'pn1', target: 'pn3', cluster: 'a' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes77, - patternWith3Nodes, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log('77 nodes graph matching 3 nodes pattern', performance.now() - begin); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - - expect(result.length).toBe(5); - expect(result[0].nodes[0].id).toBe('11'); - expect(result[1].nodes[0].id).toBe('48'); - expect(result[2].nodes[0].id).toBe('60'); - expect(result[3].nodes[0].id).toBe('63'); - expect(result[4].nodes[0].id).toBe('66'); - - expect(result[3].nodes.length).toBe(4); - expect(result[4].nodes.length).toBe(3); - }); - // 100ms - it('pattern 5 nodes', () => { - const patternWith5Nodes = { - nodes: [ - { id: 'pn1', cluster: 'A' }, - { id: 'pn2', cluster: 'C' }, - { id: 'pn3', cluster: 'C' }, - { id: 'pn4', cluster: 'B' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'c' }, - { source: 'pn1', target: 'pn3', cluster: 'c' }, - { source: 'pn3', target: 'pn2', cluster: 'c' }, - { source: 'pn3', target: 'pn4', cluster: 'b' }, - { source: 'pn2', target: 'pn4', cluster: 'b' }, - { source: 'pn1', target: 'pn4', cluster: 'c' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes77, - patternWith5Nodes, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log('77 nodes graph matching 5 nodes pattern', performance.now() - begin); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - expect(result.length).toBe(2); - expect(result[0].nodes[0].id).toBe('11'); - expect(result[1].nodes[0].id).toBe('26'); - - expect(result[0].nodes.length).toBe(7); - expect(result[1].nodes.length).toBe(4); - }); - it('pattern 10 nodes', () => { - const patternWith10Nodes = { - nodes: [ - { id: 'pn1', cluster: 'B' }, - { id: 'pn2', cluster: 'B' }, - { id: 'pn3', cluster: 'B' }, - { id: 'pn4', cluster: 'C' }, - { id: 'pn5', cluster: 'B' }, - { id: 'pn6', cluster: 'A' }, - { id: 'pn7', cluster: 'B' }, - { id: 'pn8', cluster: 'B' }, - { id: 'pn9', cluster: 'A' }, - { id: 'pn10', cluster: 'B' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'b' }, - { source: 'pn1', target: 'pn3', cluster: 'b' }, - { source: 'pn1', target: 'pn4', cluster: 'b' }, - { source: 'pn1', target: 'pn5', cluster: 'b' }, - { source: 'pn1', target: 'pn6', cluster: 'b' }, - { source: 'pn1', target: 'pn7', cluster: 'b' }, - { source: 'pn1', target: 'pn8', cluster: 'b' }, - - { source: 'pn2', target: 'pn3', cluster: 'b' }, - { source: 'pn2', target: 'pn4', cluster: 'b' }, - { source: 'pn2', target: 'pn5', cluster: 'b' }, - { source: 'pn2', target: 'pn6', cluster: 'b' }, - { source: 'pn2', target: 'pn7', cluster: 'b' }, - { source: 'pn2', target: 'pn8', cluster: 'b' }, - - { source: 'pn3', target: 'pn4', cluster: 'b' }, - { source: 'pn3', target: 'pn5', cluster: 'b' }, - { source: 'pn3', target: 'pn6', cluster: 'b' }, - { source: 'pn3', target: 'pn7', cluster: 'b' }, - { source: 'pn3', target: 'pn8', cluster: 'b' }, - - { source: 'pn4', target: 'pn5', cluster: 'b' }, - { source: 'pn4', target: 'pn6', cluster: 'b' }, - { source: 'pn4', target: 'pn7', cluster: 'b' }, - { source: 'pn4', target: 'pn8', cluster: 'b' }, - - { source: 'pn5', target: 'pn6', cluster: 'b' }, - { source: 'pn5', target: 'pn7', cluster: 'b' }, - { source: 'pn5', target: 'pn8', cluster: 'b' }, - - { source: 'pn6', target: 'pn7', cluster: 'b' }, - { source: 'pn6', target: 'pn8', cluster: 'b' }, - - { source: 'pn7', target: 'pn8', cluster: 'b' }, - - { source: 'pn8', target: 'pn9', cluster: 'a' }, - { source: 'pn8', target: 'pn10', cluster: 'a' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes77, - patternWith10Nodes, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log( - '77 nodes graph matching 10 nodes pattern', - performance.now() - begin, - result.length, - ); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - expect(result.length).toBe(3); - - expect(result[0].nodes[0].id).toBe('16'); - expect(result[1].nodes[0].id).toBe('17'); - expect(result[2].nodes[0].id).toBe('23'); - }); -}); - -describe('Performance: 202 nodes G', () => { - it('pattern with 4 nodes', () => { - const patternWith4Nodes = { - nodes: [ - { id: 'pn1', cluster: 'E' }, - { id: 'pn2', cluster: 'D' }, - { id: 'pn3', cluster: 'B' }, - { id: 'pn4', cluster: 'B' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'c' }, - { source: 'pn2', target: 'pn3', cluster: 'a' }, - { source: 'pn3', target: 'pn4', cluster: 'a' }, - { source: 'pn1', target: 'pn4', cluster: 'c' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes202, - patternWith4Nodes, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log('202 nodes graph matching 4 nodes pattern', performance.now() - begin); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - expect(result.length).toBe(1); - expect(result[0].nodes[0].id).toBe('67'); - expect(result[0].nodes.length).toBe(4); - }); - it('pattern with 7 nodes', () => { - const patternWith7Nodes = { - nodes: [ - { id: 'pn1', cluster: 'B' }, - { id: 'pn2', cluster: 'C' }, - { id: 'pn3', cluster: 'C' }, - { id: 'pn4', cluster: 'D' }, - { id: 'pn5', cluster: 'E' }, - { id: 'pn6', cluster: 'C' }, - { id: 'pn7', cluster: 'B' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'c' }, - { source: 'pn2', target: 'pn3', cluster: 'c' }, - { source: 'pn3', target: 'pn4', cluster: 'a' }, - { source: 'pn4', target: 'pn5', cluster: 'c' }, - { source: 'pn5', target: 'pn1', cluster: 'c' }, - { source: 'pn4', target: 'pn6', cluster: 'a' }, - { source: 'pn4', target: 'pn7', cluster: 'a' }, - { source: 'pn6', target: 'pn7', cluster: 'c' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes202, - patternWith7Nodes, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log('202 nodes graph matching 7 nodes pattern', performance.now() - begin); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - expect(result.length).toBe(6); - expect(result[0].nodes[0].id).toBe('2'); - expect(result[1].nodes[0].id).toBe('77'); - expect(result[2].nodes[0].id).toBe('87'); - expect(result[3].nodes[0].id).toBe('132'); - expect(result[4].nodes[0].id).toBe('157'); - expect(result[5].nodes[0].id).toBe('167'); - }); - it('pattern with 7 nodes, directed', () => { - const patternWith7Nodes = { - nodes: [ - { id: 'pn1', cluster: 'B' }, - { id: 'pn2', cluster: 'C' }, - { id: 'pn3', cluster: 'C' }, - { id: 'pn4', cluster: 'D' }, - { id: 'pn5', cluster: 'E' }, - { id: 'pn6', cluster: 'C' }, - { id: 'pn7', cluster: 'B' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'c' }, - { source: 'pn1', target: 'pn5', cluster: 'c' }, - { source: 'pn3', target: 'pn2', cluster: 'c' }, - { source: 'pn4', target: 'pn3', cluster: 'a' }, - { source: 'pn5', target: 'pn4', cluster: 'c' }, - { source: 'pn4', target: 'pn6', cluster: 'a' }, - { source: 'pn4', target: 'pn7', cluster: 'a' }, - { source: 'pn7', target: 'pn6', cluster: 'c' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes202, - patternWith7Nodes, - true, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log('202 nodes graph matching 7 nodes pattern, directed', performance.now() - begin); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - expect(result.length).toBe(1); - expect(result[0].nodes[0].id).toBe('167'); - expect(result[0].nodes.length).toBe(7); - }); - it('pattern with 14 nodes, directed', () => { - const patternWith14Nodes = { - nodes: [ - { id: 'pn1', cluster: 'D' }, - { id: 'pn2', cluster: 'C' }, - { id: 'pn3', cluster: 'D' }, - { id: 'pn4', cluster: 'D' }, - { id: 'pn5', cluster: 'C' }, - { id: 'pn6', cluster: 'B' }, - { id: 'pn7', cluster: 'E' }, - { id: 'pn8', cluster: 'C' }, - { id: 'pn9', cluster: 'C' }, - { id: 'pn10', cluster: 'B' }, - { id: 'pn11', cluster: 'B' }, - { id: 'pn12', cluster: 'A' }, - { id: 'pn13', cluster: 'E' }, - { id: 'pn14', cluster: 'B' }, - ], - edges: [ - { source: 'pn2', target: 'pn1', cluster: 'c' }, - { source: 'pn3', target: 'pn1', cluster: 'a' }, - { source: 'pn4', target: 'pn1', cluster: 'c' }, - { source: 'pn1', target: 'pn5', cluster: 'c' }, - { source: 'pn6', target: 'pn1', cluster: 'c' }, - { source: 'pn7', target: 'pn1', cluster: 'c' }, - { source: 'pn8', target: 'pn1', cluster: 'c' }, - { source: 'pn1', target: 'pn9', cluster: 'a' }, - { source: 'pn10', target: 'pn1', cluster: 'c' }, - { source: 'pn11', target: 'pn1', cluster: 'a' }, - { source: 'pn1', target: 'pn12', cluster: 'c' }, - { source: 'pn1', target: 'pn13', cluster: 'c' }, - { source: 'pn14', target: 'pn13', cluster: 'c' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes202, - patternWith14Nodes, - true, - undefined, - undefined, - 'cluster', - 'cluster', - ); - - console.log( - '202 nodes graph matching 14 nodes pattern, directed', - performance.now() - begin, - result.length, - ); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - // expect(result.length).toBe(8); - // expect(result[7].nodes[0].id).toBe("100"); - }); -}); -describe('Performance: 1589 nodes G', () => { - it('pattern with 4 nodes', () => { - const patternWith4Nodes = { - nodes: [ - { id: 'pn1', cluster: 'A' }, - { id: 'pn2', cluster: 'B' }, - { id: 'pn3', cluster: 'C' }, - { id: 'pn4', cluster: 'A' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'B' }, - { source: 'pn1', target: 'pn3', cluster: 'C' }, - { source: 'pn2', target: 'pn3', cluster: 'A' }, - { source: 'pn3', target: 'pn4', cluster: 'C' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes1589, - patternWith4Nodes, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log( - '1589 nodes graph matching 4 nodes pattern', - performance.now() - begin, - result.length, - ); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - - expect(result.length).toBe(163); - }); - // TODO: 爆栈 - xit('pattern with 6 nodes full-connected', () => { - const patternWith6Nodes = { - nodes: [ - { id: 'pn1', cluster: 'B' }, - { id: 'pn2', cluster: 'C' }, - { id: 'pn3', cluster: 'A' }, - { id: 'pn4', cluster: 'C' }, - { id: 'pn4', cluster: 'B' }, - { id: 'pn4', cluster: 'A' }, - ], - edges: [ - { source: 'pn1', target: 'pn2', cluster: 'A' }, - { source: 'pn1', target: 'pn3', cluster: 'B' }, - { source: 'pn1', target: 'pn4', cluster: 'A' }, - { source: 'pn1', target: 'pn5', cluster: 'C' }, - { source: 'pn1', target: 'pn6', cluster: 'B' }, - - { source: 'pn2', target: 'pn3', cluster: 'C' }, - { source: 'pn2', target: 'pn4', cluster: 'B' }, - { source: 'pn2', target: 'pn5', cluster: 'A' }, - { source: 'pn2', target: 'pn6', cluster: 'C' }, - - { source: 'pn3', target: 'pn3', cluster: 'C' }, - { source: 'pn3', target: 'pn5', cluster: 'B' }, - { source: 'pn3', target: 'pn6', cluster: 'A' }, - - { source: 'pn4', target: 'pn5', cluster: 'A' }, - { source: 'pn4', target: 'pn6', cluster: 'C' }, - - { source: 'pn5', target: 'pn6', cluster: 'B' }, - ], - }; - const begin = performance.now(); - const result = GADDI( - nodes1589, - patternWith6Nodes, - false, - undefined, - undefined, - 'cluster', - 'cluster', - ); - console.log( - '1589 nodes graph matching 6 nodes full-connected pattern', - performance.now() - begin, - result.length, - ); - result.forEach(re => { - console.log(JSON.stringify(re)); - }); - }); -}); - -describe('Prune', () => { - it('Prune', () => { - const dataPrune = { - "nodes":[ - { - "id":"0", - "cluster":"B" - }, - { - "id":"1", - "cluster":"B" - }, - { - "id":"2", - "cluster":"B" - }, - { - "id":"3", - "cluster":"C" - }, - ], - "edges":[ - { - "source":"0", - "target":"1", - "cluster":"b" - }, - { - "source":"1", - "target":"2", - "cluster":"b" - }, - { - "source":"1", - "target":"3", - "cluster":"b" - }, - ] - } - - const dataPrune2 = { - "nodes":[ - { - "id":"0", - "cluster":"B" - }, - { - "id":"1", - "cluster":"B" - }, - { - "id":"2", - "cluster":"B" - }, - { - "id":"3", - "cluster":"B" - }, - { - "id":"4", - "cluster":"B" - }, - { - "id":"5", - "cluster":"B" - }, - { - "id":"6", - "cluster":"B" - }, - { - "id":"7", - "cluster":"B" - }, - { - "id":"8", - "cluster":"B" - }, - { - "id":"9", - "cluster":"B" - }, - { - "id":"10", - "cluster":"B" - }, - { - "id":"11", - "cluster":"C" - }, - ], - "edges":[ - { - "source":"0", - "target":"6", - "cluster":"b" - }, - { - "source":"1", - "target":"6", - "cluster":"b" - }, - { - "source":"2", - "target":"6", - "cluster":"b" - }, - { - "source":"3", - "target":"6", - "cluster":"b" - }, - { - "source":"4", - "target":"6", - "cluster":"b" - }, - { - "source":"5", - "target":"6", - "cluster":"b" - }, - { - "source":"6", - "target":"7", - "cluster":"b" - }, - { - "source":"6", - "target":"8", - "cluster":"b" - }, - { - "source":"6", - "target":"9", - "cluster":"b" - }, - { - "source":"6", - "target":"10", - "cluster":"b" - }, - { - "source":"6", - "target":"11", - "cluster":"b" - }, - ] - } - const p = { - nodes: [ - { - id: '0', - cluster: 'B', - }, - { - id: '1', - cluster: 'B', - }, - { - id: '2', - cluster: 'B', - }, - { - id: '3', - cluster: 'C', - }, - ], - edges: [ - { - source: '0', - target: '1', - cluster: 'b', - }, - { - source: '2', - target: '1', - cluster: 'b', - }, - { - source: '1', - target: '3', - cluster: 'b', - }, - ], - }; - - const result = GADDI( - dataPrune2, - p, - true, - undefined, - undefined, - 'cluster', - 'cluster', - ); - - // console.log('res', result); - // // console.log(JSON.stringify(data3)); - - // result.forEach(re => { - // console.log(JSON.stringify(re)); - // }); - expect(result.length).toBe(1); - }) -}); \ No newline at end of file diff --git a/packages/graph/tests/unit/kMeans-spec.ts b/packages/graph/tests/unit/kMeans-spec.ts deleted file mode 100644 index 7e9ed1d..0000000 --- a/packages/graph/tests/unit/kMeans-spec.ts +++ /dev/null @@ -1,236 +0,0 @@ -import { kMeans } from '../../src'; -import { GraphData, NodeConfig } from '../../src/types'; -import propertiesGraphData from './data/cluster-origin-properties-data.json'; - -describe('kMeans abnormal demo', () => { - it('no properties demo: ', () => { - const noPropertiesData = { - nodes: [ - { - id: 'node-0', - }, - { - id: 'node-1', - }, - { - id: 'node-2', - }, - { - id: 'node-3', - } - ], - edges: [], - } - const { clusters, clusterEdges } = kMeans(noPropertiesData, 2); - expect(clusters.length).toBe(1); - expect(clusterEdges.length).toBe(0); - }); -}); - -describe('kMeans normal demo', () => { - const simpleGraphData = { - nodes: [ - { - id: 'node-0', - properties: { - amount: 10, - city: '10001', - } - }, - { - id: 'node-1', - properties: { - amount: 10000, - city: '10002', - } - }, - { - id: 'node-2', - properties: { - amount: 3000, - city: '10003', - } - }, - { - id: 'node-3', - properties: { - amount: 3200, - city: '10003', - } - }, - { - id: 'node-4', - properties: { - amount: 2000, - city: '10003', - } - } - ], - edges: [ - { - id: 'edge-0', - source: 'node-0', - target: 'node-1', - }, - { - id: 'edge-1', - source: 'node-0', - target: 'node-2', - }, - { - id: 'edge-4', - source: 'node-3', - target: 'node-2', - }, - { - id: 'edge-5', - source: 'node-2', - target: 'node-1', - }, - { - id: 'edge-6', - source: 'node-4', - target: 'node-1', - }, - ] - } - it('simple data demo: ', () => { - const nodes = simpleGraphData.nodes as NodeConfig[]; - const { clusters } = kMeans(simpleGraphData, 3, 'properties'); - expect(clusters.length).toBe(3); - expect(nodes[2].clusterId).toEqual(nodes[3].clusterId); - expect(nodes[2].clusterId).toEqual(nodes[4].clusterId); - }); - - it('complex data demo: ', () => { - const nodes = propertiesGraphData.nodes as NodeConfig[]; - const { clusters } = kMeans(propertiesGraphData as GraphData, 3, 'properties'); - expect(clusters.length).toBe(3); - expect(nodes[0].clusterId).toEqual(nodes[1].clusterId); - expect(nodes[0].clusterId).toEqual(nodes[2].clusterId); - expect(nodes[0].clusterId).toEqual(nodes[3].clusterId); - expect(nodes[0].clusterId).toEqual(nodes[4].clusterId); - expect(nodes[5].clusterId).toEqual(nodes[6].clusterId); - expect(nodes[5].clusterId).toEqual(nodes[7].clusterId); - expect(nodes[5].clusterId).toEqual(nodes[8].clusterId); - expect(nodes[5].clusterId).toEqual(nodes[9].clusterId); - expect(nodes[5].clusterId).toEqual(nodes[10].clusterId); - expect(nodes[11].clusterId).toEqual(nodes[12].clusterId); - expect(nodes[11].clusterId).toEqual(nodes[13].clusterId); - expect(nodes[11].clusterId).toEqual(nodes[14].clusterId); - expect(nodes[11].clusterId).toEqual(nodes[15].clusterId); - expect(nodes[11].clusterId).toEqual(nodes[16].clusterId); - }); - - - it('demo use involvedKeys: ', () => { - const involvedKeys = ['amount']; - const nodes = simpleGraphData.nodes as NodeConfig[]; - const { clusters } = kMeans(simpleGraphData, 3, 'properties', involvedKeys); - expect(clusters.length).toBe(3); - expect(nodes[2].clusterId).toEqual(nodes[3].clusterId); - expect(nodes[2].clusterId).toEqual(nodes[4].clusterId); - }); - - it('demo use uninvolvedKeys: ', () => { - const uninvolvedKeys = ['id', 'city']; - const nodes = simpleGraphData.nodes as NodeConfig[]; - const { clusters } = kMeans(simpleGraphData, 3, 'properties', [], uninvolvedKeys); - expect(clusters.length).toBe(3); - expect(nodes[2].clusterId).toEqual(nodes[3].clusterId); - expect(nodes[2].clusterId).toEqual(nodes[4].clusterId); - }); -}); - -describe('kMeans All properties values are numeric demo', () => { - it('all properties values are numeric demo: ', () => { - const allPropertiesValuesNumericData = { - nodes: [ - { - id: 'node-0', - properties: { - max: 1000000, - mean: 900000, - min: 800000, - } - }, - { - id: 'node-1', - properties: { - max: 1600000, - mean: 1100000, - min: 600000, - } - }, - { - id: 'node-2', - properties: { - max: 5000, - mean: 3500, - min: 2000, - } - }, - { - id: 'node-3', - properties: { - max: 9000, - mean: 7500, - min: 6000, - } - } - ], - edges: [], - } - const { clusters, clusterEdges } = kMeans(allPropertiesValuesNumericData, 2, 'properties'); - expect(clusters.length).toBe(2); - expect(clusterEdges.length).toBe(0); - const nodes = allPropertiesValuesNumericData.nodes as NodeConfig[]; - expect(nodes[0].clusterId).toEqual(nodes[1].clusterId); - expect(nodes[2].clusterId).toEqual(nodes[3].clusterId); - }); - - it('only one property and the value are numeric demo: ', () => { - const allPropertiesValuesNumericData = { - nodes: [ - { - id: 'node-0', - properties: { - num: 10, - } - }, - { - id: 'node-1', - properties: { - num: 12, - } - }, - { - id: 'node-2', - properties: { - num: 56, - } - }, - { - id: 'node-3', - properties: { - num: 300, - } - }, - { - id: 'node-4', - properties: { - num: 350, - } - } - ], - edges: [], - } - const { clusters, clusterEdges } = kMeans(allPropertiesValuesNumericData, 2, 'properties'); - expect(clusters.length).toBe(2); - expect(clusterEdges.length).toBe(0); - const nodes = allPropertiesValuesNumericData.nodes as NodeConfig[]; - expect(nodes[0].clusterId).toEqual(nodes[1].clusterId); - expect(nodes[0].clusterId).toEqual(nodes[2].clusterId); - expect(nodes[3].clusterId).toEqual(nodes[4].clusterId); - }); -}); diff --git a/packages/graph/tests/unit/label-propagation-async-spec.ts b/packages/graph/tests/unit/label-propagation-async-spec.ts deleted file mode 100644 index f2d5401..0000000 --- a/packages/graph/tests/unit/label-propagation-async-spec.ts +++ /dev/null @@ -1,89 +0,0 @@ -import { getAlgorithm } from './utils'; -import { GraphData } from '../../src/types'; - -describe('(Async) label propagation', () => { - it('simple label propagation', async done => { - const { labelPropagationAsync } = await getAlgorithm(); - const data: GraphData = { - nodes: [ - { id: '0' }, - { id: '1' }, - { id: '2' }, - { id: '3' }, - { id: '4' }, - { id: '5' }, - { id: '6' }, - { id: '7' }, - { id: '8' }, - { id: '9' }, - { id: '10' }, - { id: '11' }, - { id: '12' }, - { id: '13' }, - { id: '14' }, - ], - edges: [ - { source: '0', target: '1' }, - { source: '0', target: '2' }, - { source: '0', target: '3' }, - { source: '0', target: '4' }, - { source: '1', target: '2' }, - { source: '1', target: '3' }, - { source: '1', target: '4' }, - { source: '2', target: '3' }, - { source: '2', target: '4' }, - { source: '3', target: '4' }, - { source: '0', target: '0' }, - { source: '0', target: '0' }, - { source: '0', target: '0' }, - - { source: '5', target: '6', weight: 5 }, - { source: '5', target: '7' }, - { source: '5', target: '8' }, - { source: '5', target: '9' }, - { source: '6', target: '7' }, - { source: '6', target: '8' }, - { source: '6', target: '9' }, - { source: '7', target: '8' }, - { source: '7', target: '9' }, - { source: '8', target: '9' }, - - { source: '10', target: '11' }, - { source: '10', target: '12' }, - { source: '10', target: '13' }, - { source: '10', target: '14' }, - { source: '11', target: '12' }, - { source: '11', target: '13' }, - { source: '11', target: '14' }, - { source: '12', target: '13' }, - { source: '12', target: '14' }, - { source: '13', target: '14', weight: 5 }, - - { source: '0', target: '5' }, - { source: '5', target: '10' }, - { source: '10', target: '0' }, - { source: '10', target: '0' }, - ], - }; - const clusteredData = await labelPropagationAsync(data, false, 'weight'); - expect(clusteredData.clusters.length).not.toBe(0); - expect(clusteredData.clusterEdges.length).not.toBe(0); - done(); - }); - it('label propagation with large graph', () => { - // https://gw.alipayobjects.com/os/antvdemo/assets/data/relations.json - fetch('https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json') - .then(res => res.json()) - .then(async data => { - // 1589 nodes, 2747 edges - const { labelPropagationAsync } = await getAlgorithm(); - const clusteredData = await labelPropagationAsync(data, false, 'weight'); - // console.log(`Call to doSomething took ${t1 - t0} milliseconds.`); - - // 9037.91999999521 ms - - expect(clusteredData.clusters.length).toBe(472); - expect(clusteredData.clusterEdges.length).toBe(444); - }); - }); -}); diff --git a/packages/graph/tests/unit/label-propagation-spec.ts b/packages/graph/tests/unit/label-propagation-spec.ts deleted file mode 100644 index 87ad625..0000000 --- a/packages/graph/tests/unit/label-propagation-spec.ts +++ /dev/null @@ -1,54 +0,0 @@ -import { labelPropagation } from '../../src'; -import { GraphData } from '../../src/types'; - -describe('label propagation', () => { - it('simple label propagation', () => { - const data: GraphData = { - nodes: [ - { id: '0' }, { id: '1' }, { id: '2' }, { id: '3' }, { id: '4' }, - { id: '5' }, { id: '6' }, { id: '7' }, { id: '8' }, { id: '9' }, - { id: '10' }, { id: '11' }, { id: '12' }, { id: '13' }, { id: '14' }, - ], - edges: [ - { source: '0', target: '1' }, { source: '0', target: '2' }, { source: '0', target: '3' }, { source: '0', target: '4' }, - { source: '1', target: '2' }, { source: '1', target: '3' }, { source: '1', target: '4' }, - { source: '2', target: '3' }, { source: '2', target: '4' }, - { source: '3', target: '4' }, - { source: '0', target: '0' }, - { source: '0', target: '0' }, - { source: '0', target: '0' }, - - { source: '5', target: '6', weight: 5 }, { source: '5', target: '7' }, { source: '5', target: '8' }, { source: '5', target: '9' }, - { source: '6', target: '7' }, { source: '6', target: '8' }, { source: '6', target: '9' }, - { source: '7', target: '8' }, { source: '7', target: '9' }, - { source: '8', target: '9' }, - - { source: '10', target: '11' }, { source: '10', target: '12' }, { source: '10', target: '13' }, { source: '10', target: '14' }, - { source: '11', target: '12' }, { source: '11', target: '13' }, { source: '11', target: '14' }, - { source: '12', target: '13' }, { source: '12', target: '14' }, - { source: '13', target: '14', weight: 5 }, - - { source: '0', target: '5' }, - { source: '5', target: '10' }, - { source: '10', target: '0' }, - { source: '10', target: '0' }, - ] - } - const clusteredData = labelPropagation(data, false, 'weight'); - expect(clusteredData.clusters.length).not.toBe(0); - expect(clusteredData.clusterEdges.length).not.toBe(0); - }); - it('label propagation with large graph', () => { // https://gw.alipayobjects.com/os/antvdemo/assets/data/relations.json - fetch('https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json') - .then((res) => res.json()) - .then((data) => { // 1589 nodes, 2747 edges - const clusteredData = labelPropagation(data, false, 'weight'); - // console.log(`Call to doSomething took ${t1 - t0} milliseconds.`); - - // 9037.91999999521 ms - - expect(clusteredData.clusters.length).toBe(472); - expect(clusteredData.clusterEdges.length).toBe(444); - }); - }); -}); diff --git a/packages/graph/tests/unit/linked-list-spec.ts b/packages/graph/tests/unit/linked-list-spec.ts deleted file mode 100644 index 381ab27..0000000 --- a/packages/graph/tests/unit/linked-list-spec.ts +++ /dev/null @@ -1,88 +0,0 @@ -import LinkedList, { LinkedListNode } from '../../src/structs/linked-list' - -describe('linked list struct', () => { - it('init linedListNode', () => { - const linkedNode1 = new LinkedListNode(1) - const linkedNode2 = new LinkedListNode(2, linkedNode1) - - expect(linkedNode1.value).toBe(1) - expect(linkedNode1.next).toBe(null) - expect(linkedNode1.toString()).toEqual('1') - - expect(linkedNode2.value).toBe(2) - expect(linkedNode2.next).toBe(linkedNode1) - expect(linkedNode2.toString()).toBe('2') - }) - - const linkedList = new LinkedList() - it('init linked list', () => { - expect(linkedList).not.toBe(undefined) - }) - - it('find & append', () => { - let node1 = linkedList.find({ value: 1 }) - expect(node1).toBe(null) - - // append node - linkedList.append(1) - node1 = linkedList.find({ value: 1 }) - - expect(node1).not.toBe(null) - expect(node1.value).toBe(1) - }) - - it('prepend', () => { - linkedList.prepend(2) - - const node1 = linkedList.find({ value: 1 }) - const node2 = linkedList.find({ value: 2 }) - expect(linkedList.toArray()).toEqual([node2, node1]) - expect(linkedList.toString()).toEqual('2,1') - }) - - it('deleteHead', () => { - const deleteHead = linkedList.deleteHead() - expect(deleteHead).not.toBe(undefined) - expect(deleteHead.value).toEqual(2) - expect(deleteHead.next).toEqual({ next: null, value: 1 }) - }) - - it('deleteTail', () => { - linkedList.prepend(3) - - const deleteTail = linkedList.deleteTail() - expect(deleteTail).not.toBe(undefined) - expect(deleteTail.value).toBe(1) - expect(deleteTail.next).toBe(null) - }) - - it('delete', () => { - linkedList.append(5) - linkedList.append(6) - - const node3 = linkedList.find({ value: 3 }) - const node5 = linkedList.find({ value: 5 }) - const node6 = linkedList.find({ value: 6 }) - expect(linkedList.toArray()).toEqual([node3, node5, node6]) - expect(linkedList.toString()).toEqual('3,5,6') - - // 删除一个不存在的元素 - let deleteNode = linkedList.delete(8) - expect(deleteNode).toBe(null) - - // 删除存在的元素 - deleteNode = linkedList.delete(5) - expect(deleteNode).not.toBe(null) - expect(deleteNode.value).toBe(5) - expect(deleteNode.next).toEqual({ next: null, value: 6 }) - - deleteNode = linkedList.find({ value: 5 }) - expect(deleteNode).toBe(null) - }) - - it('reverse', () => { - expect(linkedList.toString()).toEqual('3,6') - linkedList.reverse() - expect(linkedList.toString()).toEqual('6,3') - }) -}) \ No newline at end of file diff --git a/packages/graph/tests/unit/louvain-async-spec.ts b/packages/graph/tests/unit/louvain-async-spec.ts deleted file mode 100644 index ae0b39e..0000000 --- a/packages/graph/tests/unit/louvain-async-spec.ts +++ /dev/null @@ -1,67 +0,0 @@ -import { louvain } from '../../src'; -import { GraphData } from '../../src/types'; -import propertiesGraphData from './data/cluster-origin-properties-data.json'; - -describe('(Async) louvain', () => { - - it('simple louvain', () => { - const data: GraphData = { - nodes: [ - { id: '0' }, { id: '1' }, { id: '2' }, { id: '3' }, { id: '4' }, - { id: '5' }, { id: '6' }, { id: '7' }, { id: '8' }, { id: '9' }, - { id: '10' }, { id: '11' }, { id: '12' }, { id: '13' }, { id: '14' }, - ], - edges: [ - { source: '0', target: '1' }, { source: '0', target: '2' }, { source: '0', target: '3' }, { source: '0', target: '4' }, - { source: '1', target: '2' }, { source: '1', target: '3' }, { source: '1', target: '4' }, - { source: '2', target: '3' }, { source: '2', target: '4' }, - { source: '3', target: '4' }, - { source: '0', target: '0' }, - { source: '0', target: '0' }, - { source: '0', target: '0' }, - - { source: '5', target: '6', weight: 5 }, { source: '5', target: '7' }, { source: '5', target: '8' }, { source: '5', target: '9' }, - { source: '6', target: '7' }, { source: '6', target: '8' }, { source: '6', target: '9' }, - { source: '7', target: '8' }, { source: '7', target: '9' }, - { source: '8', target: '9' }, - - { source: '10', target: '11' }, { source: '10', target: '12' }, { source: '10', target: '13' }, { source: '10', target: '14' }, - { source: '11', target: '12' }, { source: '11', target: '13' }, { source: '11', target: '14' }, - { source: '12', target: '13' }, { source: '12', target: '14' }, - { source: '13', target: '14', weight: 5 }, - - { source: '0', target: '5' }, - { source: '5', target: '10' }, - { source: '10', target: '0' }, - { source: '10', target: '0' }, - ] - } - const clusteredData = louvain(data, false, 'weight'); - expect(clusteredData.clusters.length).toBe(3); - expect(clusteredData.clusters[0].sumTot).toBe(3); - expect(clusteredData.clusters[1].sumTot).toBe(2); - expect(clusteredData.clusterEdges.length).toBe(6); - expect(clusteredData.clusterEdges[0].count).toBe(13); - expect(clusteredData.clusterEdges[1].count).toBe(10); - expect(clusteredData.clusterEdges[1].weight).toBe(14); - }); - - it('louvain with large graph', () => { // https://gw.alipayobjects.com/os/antvdemo/assets/data/relations.json - fetch('https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json') - .then((res) => res.json()) - .then((data) => { // 1589 nodes, 2747 edges - const clusteredData = louvain(data, false, 'weight'); - expect(clusteredData.clusters.length).toBe(495); - expect(clusteredData.clusterEdges.length).toBe(505); - }); - }); - - it('louvain: add inertialModularity', () => { - const clusteredData = louvain(propertiesGraphData as GraphData, false, 'weight', 0.01, true, 'properties'); - expect(clusteredData.clusters.length).toBe(3); - expect(clusteredData.clusters[0].sumTot).toBe(3); - expect(clusteredData.clusters[1].sumTot).toBe(3); - expect(clusteredData.clusters[2].sumTot).toBe(4); - expect(clusteredData.clusterEdges.length).toBe(7); - }); -}); diff --git a/packages/graph/tests/unit/louvain-spec.ts b/packages/graph/tests/unit/louvain-spec.ts deleted file mode 100644 index 386e934..0000000 --- a/packages/graph/tests/unit/louvain-spec.ts +++ /dev/null @@ -1,67 +0,0 @@ -import { louvain, iLouvain } from '../../src'; -import { GraphData } from '../../src/types'; -import propertiesGraphData from './data/cluster-origin-properties-data.json'; - -describe('louvain', () => { - - it('simple louvain', () => { - const data: GraphData = { - nodes: [ - { id: '0' }, { id: '1' }, { id: '2' }, { id: '3' }, { id: '4' }, - { id: '5' }, { id: '6' }, { id: '7' }, { id: '8' }, { id: '9' }, - { id: '10' }, { id: '11' }, { id: '12' }, { id: '13' }, { id: '14' }, - ], - edges: [ - { source: '0', target: '1' }, { source: '0', target: '2' }, { source: '0', target: '3' }, { source: '0', target: '4' }, - { source: '1', target: '2' }, { source: '1', target: '3' }, { source: '1', target: '4' }, - { source: '2', target: '3' }, { source: '2', target: '4' }, - { source: '3', target: '4' }, - { source: '0', target: '0' }, - { source: '0', target: '0' }, - { source: '0', target: '0' }, - - { source: '5', target: '6', weight: 5 }, { source: '5', target: '7' }, { source: '5', target: '8' }, { source: '5', target: '9' }, - { source: '6', target: '7' }, { source: '6', target: '8' }, { source: '6', target: '9' }, - { source: '7', target: '8' }, { source: '7', target: '9' }, - { source: '8', target: '9' }, - - { source: '10', target: '11' }, { source: '10', target: '12' }, { source: '10', target: '13' }, { source: '10', target: '14' }, - { source: '11', target: '12' }, { source: '11', target: '13' }, { source: '11', target: '14' }, - { source: '12', target: '13' }, { source: '12', target: '14' }, - { source: '13', target: '14', weight: 5 }, - - { source: '0', target: '5' }, - { source: '5', target: '10' }, - { source: '10', target: '0' }, - { source: '10', target: '0' }, - ] - } - const clusteredData = louvain(data, false, 'weight'); - expect(clusteredData.clusters.length).toBe(3); - expect(clusteredData.clusters[0].sumTot).toBe(3); - expect(clusteredData.clusters[1].sumTot).toBe(2); - expect(clusteredData.clusterEdges.length).toBe(6); - expect(clusteredData.clusterEdges[0].count).toBe(13); - expect(clusteredData.clusterEdges[1].count).toBe(10); - expect(clusteredData.clusterEdges[1].weight).toBe(14); - }); - - it('louvain with large graph', () => { // https://gw.alipayobjects.com/os/antvdemo/assets/data/relations.json - fetch('https://gw.alipayobjects.com/os/basement_prod/da5a1b47-37d6-44d7-8d10-f3e046dabf82.json') - .then((res) => res.json()) - .then((data) => { // 1589 nodes, 2747 edges - const clusteredData = louvain(data, false, 'weight'); - expect(clusteredData.clusters.length).toBe(495); - expect(clusteredData.clusterEdges.length).toBe(505); - }); - }); - - it('louvain: add inertialModularity', () => { - const clusteredData = iLouvain(propertiesGraphData as GraphData, false, 'weight', 0.01, 'properties'); - expect(clusteredData.clusters.length).toBe(3); - expect(clusteredData.clusters[0].sumTot).toBe(3); - expect(clusteredData.clusters[1].sumTot).toBe(3); - expect(clusteredData.clusters[2].sumTot).toBe(4); - expect(clusteredData.clusterEdges.length).toBe(7); - }); -}); \ No newline at end of file diff --git a/packages/graph/tests/unit/mst-spec.ts b/packages/graph/tests/unit/mst-spec.ts deleted file mode 100644 index fcedaee..0000000 --- a/packages/graph/tests/unit/mst-spec.ts +++ /dev/null @@ -1,84 +0,0 @@ -import { minimumSpanningTree } from '../../src'; - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - weight: 1, - }, - { - source: 'B', - target: 'C', - weight: 1, - }, - { - source: 'A', - target: 'C', - weight: 2, - }, - { - source: 'D', - target: 'A', - weight: 3, - }, - { - source: 'D', - target: 'E', - weight: 4, - }, - { - source: 'E', - target: 'F', - weight: 2, - }, - { - source: 'F', - target: 'D', - weight: 3, - }, - ], -}; - -describe('minimumSpanningTree', () => { - it('test kruskal algorithm', () => { - let result = minimumSpanningTree(data, 'weight'); - let totalWeight = 0; - for (let edge of result) { - totalWeight += edge.weight; - } - expect(totalWeight).toEqual(10); - }); - - it('test prim algorithm', () => { - let result = minimumSpanningTree(data, 'weight', 'prim'); - let totalWeight = 0; - for (let edge of result) { - totalWeight += edge.weight; - } - expect(totalWeight).toEqual(10); - }); -}); diff --git a/packages/graph/tests/unit/nodesCosineSimilarity-spec.ts b/packages/graph/tests/unit/nodesCosineSimilarity-spec.ts deleted file mode 100644 index 470e1e6..0000000 --- a/packages/graph/tests/unit/nodesCosineSimilarity-spec.ts +++ /dev/null @@ -1,104 +0,0 @@ -import { nodesCosineSimilarity } from '../../src'; -import { NodeConfig } from '../../src/types'; -import propertiesGraphData from './data/cluster-origin-properties-data.json'; - -describe('nodesCosineSimilarity abnormal demo', () => { - it('no properties demo: ', () => { - const nodes = [ - { - id: 'node-0', - }, - { - id: 'node-1', - }, - { - id: 'node-2', - }, - { - id: 'node-3', - } - ]; - const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeConfig[], nodes[0]); - expect(allCosineSimilarity.length).toBe(3); - expect(similarNodes.length).toBe(3); - expect(allCosineSimilarity[0]).toBe(1); - expect(allCosineSimilarity[1]).toBe(0); - expect(allCosineSimilarity[2]).toBe(0); - }); -}); - -describe('nodesCosineSimilarity normal demo', () => { - it('simple demo: ', () => { - const nodes = [ - { - id: 'node-0', - properties: { - amount: 10, - } - }, - { - id: 'node-2', - properties: { - amount: 100, - } - }, - { - id: 'node-3', - properties: { - amount: 1000, - } - }, - { - id: 'node-4', - properties: { - amount: 50, - } - } - ]; - const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeConfig[], nodes[0], 'properties'); - expect(allCosineSimilarity.length).toBe(3); - expect(similarNodes.length).toBe(3); - allCosineSimilarity.forEach(data => { - expect(data).toBeGreaterThanOrEqual(0); - expect(data).toBeLessThanOrEqual(1); - }) - }); - - it('complex demo: ', () => { - const { nodes } = propertiesGraphData; - const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeConfig[], nodes[16], 'properties'); - expect(allCosineSimilarity.length).toBe(16); - expect(similarNodes.length).toBe(16); - allCosineSimilarity.forEach(data => { - expect(data).toBeGreaterThanOrEqual(0); - expect(data).toBeLessThanOrEqual(1); - }) - }); - - - it('demo use involvedKeys: ', () => { - const involvedKeys = ['amount', 'wifi']; - const { nodes } = propertiesGraphData; - const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeConfig[], nodes[16], 'properties', involvedKeys); - expect(allCosineSimilarity.length).toBe(16); - expect(similarNodes.length).toBe(16); - allCosineSimilarity.forEach(data => { - expect(data).toBeGreaterThanOrEqual(0); - expect(data).toBeLessThanOrEqual(1); - }) - expect(similarNodes[0].id).toBe('node-11'); - }); - - it('demo use uninvolvedKeys: ', () => { - const uninvolvedKeys = ['amount']; - const { nodes } = propertiesGraphData; - const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeConfig[], nodes[16], 'properties', [], uninvolvedKeys); - expect(allCosineSimilarity.length).toBe(16); - expect(similarNodes.length).toBe(16); - allCosineSimilarity.forEach(data => { - expect(data).toBeGreaterThanOrEqual(0); - expect(data).toBeLessThanOrEqual(1); - }) - expect(similarNodes[0].id).toBe('node-11'); - }); -}); diff --git a/packages/graph/tests/unit/pagerank-async-spec.ts b/packages/graph/tests/unit/pagerank-async-spec.ts deleted file mode 100644 index ab78386..0000000 --- a/packages/graph/tests/unit/pagerank-async-spec.ts +++ /dev/null @@ -1,138 +0,0 @@ -import { getAlgorithm } from './utils'; - -const data = { - nodes: [ - { - id: 'A', - label: 'A', - }, - { - id: 'B', - label: 'B', - }, - { - id: 'C', - label: 'C', - }, - { - id: 'D', - label: 'D', - }, - { - id: 'E', - label: 'E', - }, - { - id: 'F', - label: 'F', - }, - { - id: 'G', - label: 'G', - }, - { - id: 'H', - label: 'H', - }, - { - id: 'I', - label: 'I', - }, - { - id: 'J', - label: 'J', - }, - { - id: 'K', - label: 'K', - } - ], - edges: [ - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'B', - }, - { - source: 'F', - target: 'B', - }, - { - source: 'F', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'E', - target: 'D', - }, - { - source: 'E', - target: 'B', - }, - { - source: 'K', - target: 'E', - }, - { - source: 'J', - target: 'E', - }, - { - source: 'I', - target: 'E', - }, - { - source: 'H', - target: 'E', - }, - { - source: 'G', - target: 'E', - }, - { - source: 'G', - target: 'B', - }, - { - source: 'H', - target: 'B', - }, - { - source: 'I', - target: 'B', - }, - ], -}; - -describe('(Async) Calculate pagerank of graph nodes', () => { - - it('calculate pagerank', async () => { - const { pageRankAsync } = await getAlgorithm(); - const result = await pageRankAsync(data); - let maxNodeId; - let maxVal = 0; - for (let nodeId in result) { - const val = result[nodeId]; - if (val >= maxVal) { - maxNodeId = nodeId; - maxVal = val - } - } - expect(maxNodeId).toBe('B') - }); -}); diff --git a/packages/graph/tests/unit/pagerank-spec.ts b/packages/graph/tests/unit/pagerank-spec.ts deleted file mode 100644 index 8d2dc92..0000000 --- a/packages/graph/tests/unit/pagerank-spec.ts +++ /dev/null @@ -1,137 +0,0 @@ -import { pageRank } from '../../src'; - -const data = { - nodes: [ - { - id: 'A', - label: 'A', - }, - { - id: 'B', - label: 'B', - }, - { - id: 'C', - label: 'C', - }, - { - id: 'D', - label: 'D', - }, - { - id: 'E', - label: 'E', - }, - { - id: 'F', - label: 'F', - }, - { - id: 'G', - label: 'G', - }, - { - id: 'H', - label: 'H', - }, - { - id: 'I', - label: 'I', - }, - { - id: 'J', - label: 'J', - }, - { - id: 'K', - label: 'K', - } - ], - edges: [ - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'C', - target: 'B', - }, - { - source: 'F', - target: 'B', - }, - { - source: 'F', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'E', - target: 'D', - }, - { - source: 'E', - target: 'B', - }, - { - source: 'K', - target: 'E', - }, - { - source: 'J', - target: 'E', - }, - { - source: 'I', - target: 'E', - }, - { - source: 'H', - target: 'E', - }, - { - source: 'G', - target: 'E', - }, - { - source: 'G', - target: 'B', - }, - { - source: 'H', - target: 'B', - }, - { - source: 'I', - target: 'B', - }, - ], -}; - -describe('Calculate pagerank of graph nodes', () => { - - it('calculate pagerank', () => { - const result = pageRank(data); - let maxNodeId; - let maxVal = 0; - for (let nodeId in result) { - const val = result[nodeId]; - if (val >= maxVal) { - maxNodeId = nodeId; - maxVal = val - } - } - expect(maxNodeId).toBe('B') - }); -}); diff --git a/packages/graph/tests/unit/queue-spec.ts b/packages/graph/tests/unit/queue-spec.ts deleted file mode 100644 index d998481..0000000 --- a/packages/graph/tests/unit/queue-spec.ts +++ /dev/null @@ -1,65 +0,0 @@ -import Queue from '../../src/structs/queue'; - -describe('Queue', () => { - it('should create empty queue', () => { - const queue = new Queue(); - expect(queue).not.toBeNull(); - expect(queue.linkedList).not.toBeNull(); - }); - - it('should enqueue data to queue', () => { - const queue = new Queue(); - - queue.enqueue(1); - queue.enqueue(2); - - expect(queue.toString()).toBe('1,2'); - }); - - it('should be possible to enqueue/dequeue objects', () => { - const queue = new Queue(); - - queue.enqueue({ value: 'test1', key: 'key1' }); - queue.enqueue({ value: 'test2', key: 'key2' }); - - const stringifier = (value) => `${value.key}:${value.value}`; - - expect(queue.toString(stringifier)).toBe('key1:test1,key2:test2'); - expect(queue.dequeue().value).toBe('test1'); - expect(queue.dequeue().value).toBe('test2'); - }); - - it('should peek data from queue', () => { - const queue = new Queue(); - - expect(queue.peek()).toBeNull(); - - queue.enqueue(1); - queue.enqueue(2); - - expect(queue.peek()).toBe(1); - expect(queue.peek()).toBe(1); - }); - - it('should check if queue is empty', () => { - const queue = new Queue(); - - expect(queue.isEmpty()).toBe(true); - - queue.enqueue(1); - - expect(queue.isEmpty()).toBe(false); - }); - - it('should dequeue from queue in FIFO order', () => { - const queue = new Queue(); - - queue.enqueue(1); - queue.enqueue(2); - - expect(queue.dequeue()).toBe(1); - expect(queue.dequeue()).toBe(2); - expect(queue.dequeue()).toBeNull(); - expect(queue.isEmpty()).toBe(true); - }); -}); diff --git a/packages/graph/tests/unit/stack-spec.ts b/packages/graph/tests/unit/stack-spec.ts deleted file mode 100644 index c2cc948..0000000 --- a/packages/graph/tests/unit/stack-spec.ts +++ /dev/null @@ -1,85 +0,0 @@ -import Stack from '../../src/structs/stack'; - -describe('stack unit test', () => { - it('init stack', () => { - const stack = new Stack(); - for (let i = 0; i < 4; i++) { - stack.push({ - nodes: [ - { - id: `node${i}`, - }, - ], - }); - } - - const result = stack.pop(); - // console.log(stack.toArray()); - expect(result).toEqual({ - nodes: [ - { - id: 'node3', - }, - ], - }); - - expect(stack.peek()).toEqual({ - nodes: [ - { - id: 'node2', - }, - ], - }); - - expect(stack.isMaxStack()).toBe(false); - expect(stack.isEmpty()).toBe(false); - - stack.push({ - nodes: [ - { - id: 'node5', - }, - ], - }); - stack.push({ - nodes: [ - { - id: 'node6', - }, - ], - }); - - expect(stack.isMaxStack()).toBe(false); - stack.clear() - expect(stack.length).toBe(0) - }); - - it('init stack with maxStep', () => { - const stack = new Stack(3); - for (let i = 0; i < 5; i++) { - stack.push({ - nodes: [ - { - id: `node${i}`, - }, - ], - }); - } - expect(stack.length).toBe(3); - - expect(stack.toArray()).toEqual([ - { - nodes: [{ id: 'node4'}] - }, - { - nodes: [{ id: 'node3'}] - }, - { - nodes: [{ id: 'node2'}] - } - ]) - - stack.clear() - expect(stack.length).toBe(0) - }); -}); diff --git a/packages/graph/tests/unit/util-spec.ts b/packages/graph/tests/unit/util-spec.ts deleted file mode 100644 index bb2ec3f..0000000 --- a/packages/graph/tests/unit/util-spec.ts +++ /dev/null @@ -1,116 +0,0 @@ -import { getNeighbors, getEdgesByNodeId, getOutEdgesNodeId } from '../../src/util' - -const data = { - nodes: [ - { - id: 'A', - }, - { - id: 'B', - }, - { - id: 'C', - }, - { - id: 'D', - }, - { - id: 'E', - }, - { - id: 'F', - }, - { - id: 'G', - }, - { - id: 'H', - }, - ], - edges: [ - { - source: 'A', - target: 'B', - }, - { - source: 'B', - target: 'C', - }, - { - source: 'A', - target: 'C', - }, - { - source: 'D', - target: 'A', - }, - { - source: 'D', - target: 'E', - }, - { - source: 'E', - target: 'F', - }, - { - source: 'F', - target: 'D', - }, - { - source: 'G', - target: 'H', - }, - { - source: 'H', - target: 'G', - }, - ], -}; - -describe('algorithm util method', () => { - it('getNeighbors', () => { - - let result = getNeighbors('A', data.edges); - expect(result).toEqual(['B', 'C', 'D']); - - result = getNeighbors('A', data.edges, 'source') - expect(result).toEqual(['D']) - - result = getNeighbors('A', data.edges, 'target') - expect(result).toEqual(['B', 'C']) - }); - - it('getEdgesByNodeId', () => { - const aEdges = [ - { - source: 'A', - target: 'B', - }, - { - source: 'A', - target: 'C', - }, - { - source: 'D', - target: 'A', - }, - ] - let result = getEdgesByNodeId('A', data.edges); - expect(result).toEqual(aEdges); - }); - - it('getOutEdgesNodeId', () => { - const aEdges = [ - { - source: 'A', - target: 'B', - }, - { - source: 'A', - target: 'C', - } - ] - let result = getOutEdgesNodeId('A', data.edges); - expect(result).toEqual(aEdges); - }); -}); diff --git a/packages/graph/tests/unit/utils.ts b/packages/graph/tests/unit/utils.ts deleted file mode 100644 index eed2d5a..0000000 --- a/packages/graph/tests/unit/utils.ts +++ /dev/null @@ -1,20 +0,0 @@ -import algorithm from '../../src/'; - -type IAlgorithm = typeof algorithm; -declare const window: Window & { - Algorithm: IAlgorithm; -}; - -export const getAlgorithm = () => - new Promise(resolve => { - if (window.Algorithm) { - resolve(window.Algorithm); - } - const script = document.createElement('script'); - script.type = 'text/javascript'; - script.src = `${process.cwd()}/dist/index.min.js`; - script.onload = function() { - resolve(window.Algorithm); - }; - document.body.append(script); - }); \ No newline at end of file diff --git a/packages/graph/tsconfig.json b/packages/graph/tsconfig.json index ba439d3..2ccebef 100644 --- a/packages/graph/tsconfig.json +++ b/packages/graph/tsconfig.json @@ -1,4 +1,5 @@ { + "extends": "../../tsconfig.json", "compilerOptions": { "rootDir": "./src", "composite": true, diff --git a/packages/graph/webpack.config.js b/packages/graph/webpack.config.js index 4858a47..baaa168 100644 --- a/packages/graph/webpack.config.js +++ b/packages/graph/webpack.config.js @@ -1,22 +1,15 @@ -const webpack = require('webpack'); const resolve = require('path').resolve; module.exports = { - entry: { - index: './src/index.ts', - async: './src/asyncIndex.ts' - }, + entry: './src/index.ts', output: { - filename: '[name].min.js', - library: 'Algorithm', + filename: 'index.min.js', + library: 'GraphAlgorithm', libraryTarget: 'umd', - libraryExport: 'default', - path: resolve(process.cwd(), 'dist/'), + path: resolve(process.cwd(), 'dist'), globalObject: 'this', - publicPath: './dist', - }, - watchOptions: { - ignored: /node_modules/ + publicPath: '', + clean: true, }, resolve: { extensions: ['.ts', '.js'], @@ -24,50 +17,11 @@ module.exports = { module: { rules: [ { - test: /\.worker\.ts$/, - exclude: /(node_modules)/, - use: [ - { - loader: 'worker-loader', - options: { - inline: 'fallback', - filename: 'index.worker.js', - }, - }, - ], - }, - { - test: /\.js$/, - include: /node_modules/, - use: { - loader: 'babel-loader', - options: { - presets: [ - [ - '@babel/preset-env', - { - loose: true, - modules: false, - }, - ], - { - plugins: ['@babel/plugin-proposal-class-properties'], - }, - ], - }, - }, - }, - { - test: /\.ts$/, - use: { - loader: 'ts-loader', - options: { - transpileOnly: true, - }, - }, + test: /\.tsx?$/, + use: "ts-loader", + exclude: /node_modules/, }, ], }, - plugins: [new webpack.NoEmitOnErrorsPlugin(), new webpack.optimize.AggressiveMergingPlugin()], devtool: 'source-map', }; diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml new file mode 100644 index 0000000..ec32dfd --- /dev/null +++ b/pnpm-lock.yaml @@ -0,0 +1,7658 @@ +lockfileVersion: '6.0' + +importers: + + .: + devDependencies: + '@antv/graphlib': + specifier: ^2.0.0 + version: 2.0.0 + '@changesets/cli': + specifier: ^2.26.1 + version: 2.26.1 + '@types/jest': + specifier: latest + version: 29.5.2 + benchmark: + specifier: ^2.1.4 + version: 2.1.4 + cross-env: + specifier: ^7.0.3 + version: 7.0.3 + eslint: + specifier: ^7.32.0 + version: 7.32.0 + eslint-config-airbnb-base: + specifier: ^14.2.1 + version: 14.2.1(eslint-plugin-import@2.27.5)(eslint@7.32.0) + eslint-config-prettier: + specifier: ^6.15.0 + version: 6.15.0(eslint@7.32.0) + eslint-plugin-import: + specifier: ^2.27.5 + version: 2.27.5(eslint@7.32.0) + gh-pages: + specifier: ^5.0.0 + version: 5.0.0 + graphology: + specifier: ^0.25.1 + version: 0.25.1(graphology-types@0.24.7) + graphology-communities-louvain: + specifier: latest + version: 2.0.1(graphology-types@0.24.7) + graphology-generators: + specifier: ^0.11.2 + version: 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7a1e793..fde6813 100644 --- a/site/benchmark/index.html +++ b/site/benchmark/index.html @@ -140,6 +140,7 @@

Choose a dataset: +
@@ -165,13 +167,13 @@
-
-
@antv/algorithm
+
+
@antv/graph
-
-
@antv/webgpu-graph
+
+
@antv/graph-gpu
diff --git a/site/benchmark/louvain.ts b/site/benchmark/louvain.ts new file mode 100644 index 0000000..70ac41d --- /dev/null +++ b/site/benchmark/louvain.ts @@ -0,0 +1,44 @@ +import { Graph, ID } from "@antv/graphlib"; +import louvain from 'graphology-communities-louvain'; +import { louvain as antvLouvain } from "../../packages/graph"; +import { Threads } from "../../packages/graph-wasm"; +import { graph2Edgelist } from "./util"; + +/** + * @see https://graphology.github.io/standard-library/communities-louvain.html + */ +export async function graphology(graph: any) { + const mapping = louvain(graph); + const clusters: { id: number; count: number }[] = []; + Object.keys(mapping).forEach((id) => { + const clusterIdx = mapping[id]; + if (!clusters[clusterIdx]) { + clusters[clusterIdx] = { id: clusterIdx, count: 0 }; + } + clusters[clusterIdx].count++; + }); + return clusters; +} + +export async function antv( + graph: Graph, +) { + const { clusters } = antvLouvain(graph, false, 'weight'); + return clusters.map(({ id, nodes }) => ({ id, count: nodes.length })); +} + +export async function wasm( + graph: Graph, + _: any, + _2: any, + threads: Threads +): Promise { + const { edgelist, idxNodeMap } = graph2Edgelist(graph, 'weight'); + const ranks = await threads.louvain({ + edgelist: edgelist as [number, number, number][] + }); + console.log(ranks); + return ranks; + // const formatted = ranks.map((rank, i) => ({ id: idxNodeMap[i], score: rank })); + // return format(formatted.sort((a, b) => b.score - a.score).slice(0, 10)); // {id: 'Valjean', score: 0.1} +} \ No newline at end of file diff --git a/site/benchmark/main.ts b/site/benchmark/main.ts index d875fd6..e4748ec 100644 --- a/site/benchmark/main.ts +++ b/site/benchmark/main.ts @@ -3,9 +3,13 @@ import { loadDatasets } from "../datasets"; import { TestName } from "../types"; import { graphology as graphologyPageRank, + antv as antvPageRank, webgpu as webgpuPageRank, + wasm as wasmPageRank } from "./page-rank"; -import { graphology as graphologySSSP, webgpu as webgpuSSSP } from "./sssp"; +import { graphology as graphologySSSP, antv as antvSSSP, webgpu as webgpuSSSP, wasm as wasmSSSP } from "./sssp"; +import { graphology as graphologyLouvain, antv as antvLouvain, wasm as wasmLouvain } from "./louvain"; +import { initThreads } from "../../packages/graph-wasm"; const TestsConfig = [ { @@ -15,10 +19,10 @@ const TestsConfig = [ name: TestName.ANTV_ALGORITHM, }, { - name: TestName.ANTV_WEBGPU_GRAPH, + name: TestName.ANTV_GRAPH_GPU, }, { - name: TestName.ANTV_WEBGPU_GRAPH, + name: TestName.ANTV_GRAPH_WASM, }, ]; @@ -31,6 +35,13 @@ const $results = TestsConfig.map(({ name }) => { return [$div.querySelector(".console")!, $div.querySelector(".time")!]; }); +const initThreadsPool = async () => { + const singleThread = await initThreads(false); + const multiThreads = await initThreads(true); + + return [singleThread, multiThreads]; +}; + (async () => { $run.innerHTML = "Loading..."; $run.disabled = true; @@ -44,7 +55,10 @@ const $results = TestsConfig.map(({ name }) => { // initialize WebGPU context const graph = new WebGPUGraph(); - $run.innerHTML = "Run layouts"; + console.time("Init WASM threads"); + const [forceSingleThread, forceMultiThreads] = await initThreadsPool(); + console.timeEnd("Init WASM threads"); + $run.innerHTML = 'Run layouts'; $run.disabled = false; const layoutConfig: any = [ @@ -53,59 +67,61 @@ const $results = TestsConfig.map(({ name }) => { methods: { pageRank: graphologyPageRank, sssp: graphologySSSP, + louvain: graphologyLouvain, }, }, { name: TestName.ANTV_ALGORITHM, methods: { - // pageRank: graphologyForceatlas2, - // sssp: graphologyFruchterman, + pageRank: antvPageRank, + sssp: antvSSSP, + louvain: antvLouvain, }, }, { - name: TestName.ANTV_WEBGPU_GRAPH, + name: TestName.ANTV_GRAPH_GPU, methods: { - pageRank: webgpuPageRank, + // pageRank: webgpuPageRank, sssp: webgpuSSSP, }, }, { name: TestName.ANTV_GRAPH_WASM, methods: { - // pageRank: webgpuPageRank, - // sssp: webgpuSSSP, + pageRank: wasmPageRank, + sssp: wasmSSSP, + louvain: wasmLouvain, }, }, ]; - $run.onclick = async () => { + $run.onclick = () => { const dataset = datasets[$dataset.value]; const algorithmName = $algorithm.value; - let options = null; + let options = {}; if (algorithmName === "sssp") { - const graph = dataset[TestName.ANTV_WEBGPU_GRAPH]; + const graph = dataset[TestName.ANTV_ALGORITHM]; options = graph.getAllNodes()[1].id; } - await Promise.all( - layoutConfig.map(async ({ name, methods }: any, i: number) => { - if (methods[algorithmName]) { - const start = performance.now(); - const result = await methods[algorithmName]( - dataset[name], - options, - graph - ); - $results[i][1].innerHTML = `${(performance.now() - start).toFixed( - 2 - )}ms`; + layoutConfig.map(async ({ name, methods }: any, i: number) => { + if (methods[algorithmName]) { + const start = performance.now(); + const result = await methods[algorithmName]( + dataset[name], + options, + graph, + forceMultiThreads + ); + $results[i][1].innerHTML = `${(performance.now() - start).toFixed( + 2 + )}ms`; - $results[i][0].innerHTML = JSON.stringify(result); - } else { - $results[i][0].innerHTML = ""; - $results[i][1].innerHTML = "-"; - } - }) - ); + $results[i][0].innerHTML = JSON.stringify(result); + } else { + $results[i][0].innerHTML = ""; + $results[i][1].innerHTML = "-"; + } + }) }; })(); diff --git a/site/benchmark/page-rank.ts b/site/benchmark/page-rank.ts index 8e3246b..fcc9a47 100644 --- a/site/benchmark/page-rank.ts +++ b/site/benchmark/page-rank.ts @@ -1,5 +1,13 @@ +import { Graph, ID } from "@antv/graphlib"; import pagerank from "graphology-metrics/centrality/pagerank"; -import { WebGPUGraph } from "../../packages/graph-gpu/src"; +import { pageRank } from "../../packages/graph"; +import { WebGPUGraph } from "../../packages/graph-gpu"; +import { Threads } from "../../packages/graph-wasm"; +import { graph2Edgelist } from "./util"; + +function format(records: { id: ID; score: number}[]) { + return records.map(({ id, score }) => ({ id, score: score.toFixed(6) })); +} interface Options { alpha: number; @@ -25,24 +33,52 @@ export async function graphology(graph: any, options: Partial) { score: result[key], })); - return r.sort((a, b) => b.score - a.score).slice(0, 10); // {id: 'Valjean', score: 0.1} + return format(r.sort((a, b) => b.score - a.score).slice(0, 10)); // {id: 'Valjean', score: 0.1} +} + +export async function antv( + graph: Graph, + options: Partial,) { + const result = pageRank(graph, { + ...DEFAULT_OPTIONS, + ...options, + }); + + const r = Object.keys(result).map((key) => ({ + id: key, + score: result[key], + })); + + return format(r.sort((a, b) => b.score - a.score).slice(0, 10)); // {id: 'Valjean', score: 0.1} } export async function webgpu( - graph: any, + graph: Graph, options: Partial, webgpuGraph: WebGPUGraph ) { - const { alpha, tolerance, maxIterations } = { - ...DEFAULT_OPTIONS, - ...options, - }; const result = await webgpuGraph.pageRank( graph, - tolerance, - alpha, - maxIterations + { + ...DEFAULT_OPTIONS, + ...options, + } ); - return result.slice(0, 10); // {id: 'Valjean', score: 0.1} + return format(result.slice(0, 10)); // {id: 'Valjean', score: 0.1} } + +export async function wasm( + graph: Graph, + options: Partial, + _: any, + threads: Threads +): Promise { + const { edgelist, idxNodeMap } = graph2Edgelist(graph); + const ranks = await threads.pageRank({ + ...options, + edgelist: edgelist as [number, number][] + }); + const formatted = ranks.map((rank, i) => ({ id: idxNodeMap[i], score: rank })); + return format(formatted.sort((a, b) => b.score - a.score).slice(0, 10)); // {id: 'Valjean', score: 0.1} +} \ No newline at end of file diff --git a/site/benchmark/sssp.ts b/site/benchmark/sssp.ts index 77c7572..088b3e2 100644 --- a/site/benchmark/sssp.ts +++ b/site/benchmark/sssp.ts @@ -1,16 +1,47 @@ +import { Graph } from "@antv/graphlib"; import { dijkstra } from "graphology-shortest-path"; -import { WebGPUGraph } from "../../packages/graph-gpu/src"; +import { findShortestPath } from "../../packages/graph"; +import { WebGPUGraph } from "../../packages/graph-gpu"; +import { Threads } from "../../packages/graph-wasm"; +import { graph2Edgelist } from "./util"; export async function graphology(graph: any, source: string) { - return dijkstra.singleSource(graph, source, "weight"); + const result = dijkstra.singleSource(graph, source, "weight"); + return Object.keys(result).map((key) => ({ key, path: result[key] })) + .filter(({ path }) => path.length > 0) + .slice(0, 3); +} + +export async function antv(graph: Graph, source: string) { + const nodes = graph.getAllNodes(); + const paths: any[] = []; + nodes.forEach((node) => { + const p = findShortestPath(graph, source, node.id); + paths.push(p); + }); + return paths.slice(0, 3); } export async function webgpu( - graph: any, + graph: Graph, source: string, webgpuGraph: WebGPUGraph ) { const result = await webgpuGraph.sssp(graph, source, "weight"); - return result.filter((r) => r.distance !== 1000000); + return result.filter((r) => r.distance !== 1000000).slice(0, 3); } + +export async function wasm( + graph: Graph, + source: string, + _: any, + threads: Threads +): Promise { + const { edgelist, nodeIdxMap, idxNodeMap } = graph2Edgelist(graph, 'weight'); + const paths = await threads.sssp({ + startNode: nodeIdxMap[source], + edgelist: edgelist as [number, number, number][], + }); + return paths.slice(0, 3); +} \ No newline at end of file diff --git a/site/benchmark/util.ts b/site/benchmark/util.ts new file mode 100644 index 0000000..649d594 --- /dev/null +++ b/site/benchmark/util.ts @@ -0,0 +1,20 @@ +import { Graph, ID } from "@antv/graphlib"; + +export function graph2Edgelist(graph: Graph, weightPropertyName?: string) { + const edgelist: number[][] = []; + const nodeIdxMap: Record = {}; + const idxNodeMap: Record = {}; + const edges = graph.getAllEdges(); + graph.getAllNodes().forEach((node, i) => { + nodeIdxMap[node.id] = i; + idxNodeMap[i] = node.id; + }); + edges.forEach((edge) => { + if (weightPropertyName) { + edgelist.push([nodeIdxMap[edge.source], nodeIdxMap[edge.target], edge.data[weightPropertyName] || 1]); + } else { + edgelist.push([nodeIdxMap[edge.source], nodeIdxMap[edge.target]]); + } + }); + return { edgelist, nodeIdxMap, idxNodeMap }; +} \ No newline at end of file diff --git a/site/data/undirected1000.json b/site/data/undirected1000.json new file mode 100644 index 0000000..c9f8717 --- /dev/null +++ b/site/data/undirected1000.json @@ -0,0 +1 @@ +{"nodes":[{"label":"471","x":317.35546875,"y":12.529082298278809,"id":"471","attributes":{"Modularity Class":"2"},"color":"rgb(255,85,132)","size":4.0},{"label":"488","x":37.71865463256836,"y":334.5827331542969,"id":"488","attributes":{"Modularity Class":"7"},"color":"rgb(76,70,62)","size":4.0},{"label":"267","x":-181.7259521484375,"y":278.5659484863281,"id":"267","attributes":{"Modularity 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\ No newline at end of file diff --git a/site/datasets.ts b/site/datasets.ts index c65d72c..e513f70 100644 --- a/site/datasets.ts +++ b/site/datasets.ts @@ -1,11 +1,32 @@ -import Graph from "graphology"; +import Graph, { UndirectedGraph } from "graphology"; import { Graph as AntvGraph } from "@antv/graphlib"; import { clusters } from "graphology-generators/random"; import { TestName } from "./types"; - export const loadDatasets = async () => { const datasets: Record = {}; + const loadUndirected500 = async () => { + const result = await fetch("https://raw.githubusercontent.com/graphology/graphology/master/src/communities-louvain/test/datasets/undirected1000.json") + const undirected1000Data = await result.json(); + const graph = new UndirectedGraph(); + undirected1000Data.nodes.forEach((d: any) => { + graph.addNode(d.id, d); + }); + undirected1000Data.edges.forEach((d: any) => { + graph.addEdge(d.source, d.target); + }); + + const antvgraph = graphology2antv(graph); + return { + desc: + "Creates a undirected graph with 500 nodes.", + [TestName.GRAPHOLOGY]: graph, + [TestName.ANTV_ALGORITHM]: antvgraph, + [TestName.ANTV_GRAPH_GPU]: antvgraph, + [TestName.ANTV_GRAPH_WASM]: antvgraph, + }; + }; + const loadRandomClusters = () => { const NODES = 1000; const EDGES = 2000; @@ -17,17 +38,8 @@ export const loadDatasets = async () => { clusters: 5, }); graph.edges().forEach(function(edge, i) { - // graph.setEdgeAttribute(edge, "weight", i > EDGES / 2 ? 1 : 100); graph.setEdgeAttribute(edge, "weight", 1); }); - // graph.nodes().forEach(function (node) { - // graph.setNodeAttribute(node, "x", Math.random() * CANVAS_SIZE); - // graph.setNodeAttribute(node, "y", Math.random() * CANVAS_SIZE); - - // if (dimensions === 3) { - // graph.setNodeAttribute(node, "z", Math.random() * CANVAS_SIZE); - // } - // }); const antvgraph = graphology2antv(graph); @@ -36,7 +48,8 @@ export const loadDatasets = async () => { "Creates a graph with the desired number of nodes & edges and having a given number of clusters. Generated by Graphology Generators.", [TestName.GRAPHOLOGY]: graph, [TestName.ANTV_ALGORITHM]: antvgraph, - [TestName.ANTV_WEBGPU_GRAPH]: antvgraph, + [TestName.ANTV_GRAPH_GPU]: antvgraph, + [TestName.ANTV_GRAPH_WASM]: antvgraph, }; }; @@ -93,19 +106,22 @@ export const loadDatasets = async () => { return { desc, [TestName.GRAPHOLOGY]: graphlibModel, - [TestName.ANTV_ALGORITHM]: oldG6GraphFormat, - [TestName.ANTV_WEBGPU_GRAPH]: antvGraphModel, + [TestName.ANTV_ALGORITHM]: antvGraphModel, + [TestName.ANTV_GRAPH_GPU]: antvGraphModel, + [TestName.ANTV_GRAPH_WASM]: antvGraphModel, }; }; }; const [ + undirected500, randomClusters, relations, netscience, eva, regions, ] = await Promise.all([ + loadUndirected500(), loadRandomClusters(), loadG6JSON( "https://gw.alipayobjects.com/os/antvdemo/assets/data/relations.json", @@ -125,6 +141,7 @@ export const loadDatasets = async () => { )(), ]); + datasets.undirected500 = undirected500; datasets["random-clusters"] = randomClusters; datasets.relations = relations; datasets.netscience = netscience; diff --git a/site/index.html b/site/index.html index 2258375..d4c1a8d 100644 --- a/site/index.html +++ b/site/index.html @@ -8,7 +8,7 @@ Here are some examples using - @antv/algorithm. + @antv/graph.
  • Benchmarks between CPU, WASM and GPU diff --git a/site/types.ts b/site/types.ts index 75f046e..d63bbb6 100644 --- a/site/types.ts +++ b/site/types.ts @@ -1,6 +1,6 @@ export enum TestName { GRAPHOLOGY = "graphology", - ANTV_ALGORITHM = "@antv/algorithm", - ANTV_WEBGPU_GRAPH = "@antv/webgpu-graph", + ANTV_ALGORITHM = "@antv/graph", + ANTV_GRAPH_GPU = "@antv/graph-gpu", ANTV_GRAPH_WASM = "@antv/graph-wasm", } diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 0000000..cf11009 --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,12 @@ +{ + "compilerOptions": { + "noImplicitAny": true, + "module": "esnext", + "target": "es5", + "jsx": "react", + "allowJs": true, + "moduleResolution": "node", + "resolveJsonModule": true, + "allowSyntheticDefaultImports": true + } +} diff --git a/tslint.json b/tslint.json new file mode 100644 index 0000000..31d6c78 --- /dev/null +++ b/tslint.json @@ -0,0 +1,21 @@ +{ + "extends": [ + "tslint-config-airbnb", + "tslint-eslint-rules", + "tslint-config-prettier" + ], + "linterOptions": { + "exclude": ["**/es/**/*.d.ts", "**/lib/**/*.d.ts"] + }, + "rules": { + "no-construct": true, + "no-debugger": true, + "no-reference": true, + "import-name": false, + "semicolon": [true, "always"], + "arrow-parens": true, + "no-this-assignment": false, + "no-increment-decrement": false, + "object-shorthand-properties-first": false + } +} diff --git a/vite.config.js b/vite.config.js index 3a81ae7..9ca6531 100644 --- a/vite.config.js +++ b/vite.config.js @@ -16,4 +16,18 @@ export default defineConfig({ }, }, }, + plugins: [ + { + name: "isolation", + configureServer(server) { + // The multithreads version of @antv/layout-wasm needs to use SharedArrayBuffer, which should be used in a secure context. + // @see https://gist.github.com/mizchi/afcc5cf233c9e6943720fde4b4579a2b + server.middlewares.use((_req, res, next) => { + res.setHeader("Cross-Origin-Opener-Policy", "same-origin"); + res.setHeader("Cross-Origin-Embedder-Policy", "require-corp"); + next(); + }); + }, + }, + ], }); From 7138e887003f371349c4bbb2a0bb3e31e106bd12 Mon Sep 17 00:00:00 2001 From: ZqqceE Date: Thu, 7 Sep 2023 14:00:53 +0800 Subject: [PATCH 03/15] v5-kCore (#67) * feat: algorithm k-core * test: k-core unit test * docs: add annotation * fix: remove lock file --- __tests__/unit/kCore.spec.ts | 114 +++++++++++++++++++++++++++++++++++ package.json | 3 +- packages/graph/src/index.ts | 1 + packages/graph/src/k-core.ts | 23 +++++++ 4 files changed, 140 insertions(+), 1 deletion(-) create mode 100644 __tests__/unit/kCore.spec.ts create mode 100644 packages/graph/src/k-core.ts diff --git a/__tests__/unit/kCore.spec.ts b/__tests__/unit/kCore.spec.ts new file mode 100644 index 0000000..32e95a6 --- /dev/null +++ b/__tests__/unit/kCore.spec.ts @@ -0,0 +1,114 @@ +import { Edge, Graph, Node } from "@antv/graphlib"; +import { kCore } from "../../packages/graph/src"; + +const graph = new Graph({ + nodes: [ + { + id: 'A', + data: {} + }, + { + id: 'B', + data: {} + }, + { + id: 'C', + data: {} + }, + { + id: 'D', + data: {} + }, + { + id: 'E', + data: {} + }, + { + id: 'F', + data: {} + }, + { + id: 'G', + data: {} + }, + { + id: 'H', + data: {} + }, + ], + edges: [ + { + id: 'e1', + source: 'A', + target: 'B', + data: {} + }, + { + id: 'e2', + source: 'B', + target: 'C', + data: {} + }, + { + id: 'e3', + source: 'C', + target: 'G', + data: {} + }, + { + id: 'e4', + source: 'A', + target: 'D', + data: {} + }, + { + id: 'e5', + source: 'A', + target: 'E', + data: {} + }, + { + id: 'e6', + source: 'E', + target: 'F', + data: {} + }, + { + id: 'e7', + source: 'F', + target: 'D', + data: {} + }, + { + id: 'e8', + source: 'D', + target: 'E', + data: {} + }, + ], +}); + +const validateEdge = (edge: Edge, nodes: Node[]) => { + return nodes.findIndex(n => edge.source === n.id) >= 0 && nodes.findIndex(n => edge.target === n.id) >= 0; +} +describe('k-core algorithm unit test', () => { + const nodes = graph.getAllNodes(); + const edges = graph.getAllEdges(); + it('k=1', () => { + const { nodes: kNodes, edges: kEdges } = kCore(graph, 1); + expect(kNodes.length).toBe(nodes.filter(n => graph.getDegree(n.id) >= 1).length); + expect(kEdges).toStrictEqual(edges.filter(e => validateEdge(e, nodes))); + }); + + it('k=2', () => { + const { nodes: kNodes, edges: kEdges } = kCore(graph, 2); + expect(kNodes.length).toBe(nodes.filter(n => graph.getDegree(n.id) >= 2).length); + expect(kEdges).toStrictEqual(edges.filter(e => validateEdge(e, nodes))); + }); + + it('k=3', () => { + const { nodes: kNodes, edges: kEdges } = kCore(graph, 3); + expect(kNodes.length).toBe(nodes.filter(n => graph.getDegree(n.id) >= 3).length); + expect(kEdges).toStrictEqual(edges.filter(e => validateEdge(e, nodes))); + }); +}); \ No newline at end of file diff --git a/package.json b/package.json index cb0ec1a..36c2fa5 100644 --- a/package.json +++ b/package.json @@ -21,6 +21,7 @@ "build:ci": "pnpm -r run build:ci", "prepare": "husky install", "test": "jest", + "test_one": "jest ./__tests__/unit/kCore.spec.ts", "coverage": "jest --coverage", "build:site": "vite build", "deploy": "gh-pages -d site/dist", @@ -73,4 +74,4 @@ "access": "public", "registry": "https://registry.npmjs.org" } -} +} \ No newline at end of file diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index ab5387c..bcbd01f 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -2,3 +2,4 @@ export * from "./pageRank"; export * from "./findPath"; export * from "./louvain"; export * from "./iLouvain"; +export * from "./k-core"; \ No newline at end of file diff --git a/packages/graph/src/k-core.ts b/packages/graph/src/k-core.ts new file mode 100644 index 0000000..b2a2dd6 --- /dev/null +++ b/packages/graph/src/k-core.ts @@ -0,0 +1,23 @@ +import { Graph } from "./types"; + +/** +Finds the k-core of a given graph. +@param graph - The input graph. +@param k - The minimum degree required for a node to be considered part of the k-core. Default is 1. +@returns An object containing the nodes and edges of the k-core. +*/ +export function kCore( + graph: Graph, + k: number = 1,) { + const nodes = graph.getAllNodes(); + let edges = graph.getAllEdges(); + nodes.sort((a, b) => graph.getDegree(a.id, 'both') - graph.getDegree(b.id, 'both')); + let i = 0; + while (true) { + const curNode = nodes[i]; + if (graph.getDegree(curNode.id, 'both') >= k) break; + nodes.splice(i, 1);//remove node + edges = edges.filter(e => !(e.source === i || e.target === i)); + } + return { nodes, edges }; +} \ No newline at end of file From 20a07e4dffb191d2612b94a75be7c4dc03863e88 Mon Sep 17 00:00:00 2001 From: hustcc Date: Thu, 7 Sep 2023 15:16:50 +0800 Subject: [PATCH 04/15] feat: add floydWarshall algo (#68) * feat: add floydWarshall algo * chore: remove devDependencies * chore: use Map instead of object --- __tests__/unit/floydWarshall.spec.ts | 131 +++++++++++++++++++++++++++ package.json | 2 +- packages/graph/src/floydWarshall.ts | 68 ++++++++++++++ packages/graph/src/index.ts | 3 +- 4 files changed, 202 insertions(+), 2 deletions(-) create mode 100644 __tests__/unit/floydWarshall.spec.ts create mode 100644 packages/graph/src/floydWarshall.ts diff --git a/__tests__/unit/floydWarshall.spec.ts b/__tests__/unit/floydWarshall.spec.ts new file mode 100644 index 0000000..8a336a2 --- /dev/null +++ b/__tests__/unit/floydWarshall.spec.ts @@ -0,0 +1,131 @@ +import { Graph } from "@antv/graphlib"; +import { floydWarshall } from "../../packages/graph/src"; + +const graph = new Graph({ + nodes: [ + { + id: 'A', + data: { + }, + }, + { + id: 'B', + data: { + } + }, + { + id: 'C', + data: { + } + }, + { + id: 'D', + data: { + } + }, + { + id: 'E', + data: { + } + }, + { + id: 'F', + data: { + } + }, + { + id: 'G', + data: { + } + }, + { + id: 'H', + data: { + } + }, + ], + edges: [ + { + id: 'e1', + source: 'A', + target: 'B', + data: {}, + }, + { + id: 'e2', + source: 'B', + target: 'C', + data: {}, + }, + { + id: 'e3', + source: 'C', + target: 'G', + data: {}, + }, + { + id: 'e4', + source: 'A', + target: 'D', + data: {}, + }, + { + id: 'e5', + source: 'A', + target: 'E', + data: {}, + }, + { + id: 'e6', + source: 'E', + target: 'F', + data: {}, + }, + { + id: 'e7', + source: 'F', + target: 'D', + data: {}, + }, + ], +}); + +describe('Adjacency Matrix by Algorithm', () => { + it('get graph shortestpath matrix', () => { + const matrix = floydWarshall(graph); + expect(Object.keys(matrix).length).toBe(8); + const node0 = matrix[0]; + expect(node0.length).toBe(8); + expect(node0[0]).toBe(0); + expect(node0[1]).toBe(1); + expect(node0[2]).toBe(2); + expect(node0[3]).toBe(1); + expect(node0[4]).toBe(1); + expect(node0[5]).toBe(2); + expect(node0[6]).toBe(3); + expect(node0[7]).toBe(Infinity); + expect(matrix[1][7]).toBe(Infinity); + expect(matrix[2][7]).toBe(Infinity); + expect(matrix[3][7]).toBe(Infinity); + }); + + it('directed', () => { + // directed + const matrix = floydWarshall(graph, true); + expect(Object.keys(matrix).length).toBe(8); + const node0 = matrix[0]; + expect(node0.length).toBe(8); + expect(node0[0]).toBe(0); + expect(node0[1]).toBe(1); + expect(node0[2]).toBe(2); + expect(node0[3]).toBe(1); + expect(node0[4]).toBe(1); + expect(node0[5]).toBe(2); + expect(node0[6]).toBe(3); + expect(node0[7]).toBe(Infinity); + const node8 = matrix[6]; + expect(node8.length).toBe(8); + expect(node8[1]).toBe(Infinity); + expect(node8[6]).toBe(0); + }); +}); diff --git a/package.json b/package.json index 36c2fa5..c6cac2c 100644 --- a/package.json +++ b/package.json @@ -74,4 +74,4 @@ "access": "public", "registry": "https://registry.npmjs.org" } -} \ No newline at end of file +} diff --git a/packages/graph/src/floydWarshall.ts b/packages/graph/src/floydWarshall.ts new file mode 100644 index 0000000..7beb047 --- /dev/null +++ b/packages/graph/src/floydWarshall.ts @@ -0,0 +1,68 @@ +import { Graph, Matrix } from "./types"; + +function getAdjMatrix(graph: Graph, directed: boolean) { + const nodes = graph.getAllNodes(); + const matrix: Matrix[] = []; + // map node with index in data.nodes + const nodeMap = new Map(); + + if (!nodes) { + throw new Error("invalid nodes data!"); + } + + if (nodes) { + nodes.forEach((node, i) => { + nodeMap.set(node.id, i); + const row: number[] = []; + matrix.push(row); + }); + } + + const edges = graph.getAllEdges(); + if (edges) { + edges.forEach((edge) => { + const { source, target } = edge; + const sIndex = nodeMap.get(source); + const tIndex = nodeMap.get(target); + if ((!sIndex && sIndex !== 0) || (!tIndex && tIndex !== 0)) return; + matrix[sIndex][tIndex] = 1; + if (!directed) { + matrix[tIndex][sIndex] = 1; + } + }); + } + return matrix; +} + +export function floydWarshall( + graph: Graph, + directed = false, +) { + const adjacentMatrix = getAdjMatrix(graph, directed); + + const dist: Matrix[] = []; + const size = adjacentMatrix.length; + for (let i = 0; i < size; i += 1) { + dist[i] = []; + for (let j = 0; j < size; j += 1) { + if (i === j) { + dist[i][j] = 0; + } else if (adjacentMatrix[i][j] === 0 || !adjacentMatrix[i][j]) { + dist[i][j] = Infinity; + } else { + dist[i][j] = adjacentMatrix[i][j]; + } + } + } + // floyd + for (let k = 0; k < size; k += 1) { + for (let i = 0; i < size; i += 1) { + for (let j = 0; j < size; j += 1) { + if (dist[i][j] > dist[i][k] + dist[k][j]) { + dist[i][j] = dist[i][k] + dist[k][j]; + } + } + } + } + return dist; +} \ No newline at end of file diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index bcbd01f..6a22580 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -2,4 +2,5 @@ export * from "./pageRank"; export * from "./findPath"; export * from "./louvain"; export * from "./iLouvain"; -export * from "./k-core"; \ No newline at end of file +export * from "./k-core"; +export * from "./floydWarshall"; From 745bb0ffe5c1b090f45afd9610d05e1ed633f2a3 Mon Sep 17 00:00:00 2001 From: ZqqceE Date: Mon, 11 Sep 2023 10:57:55 +0800 Subject: [PATCH 05/15] feat: algorithm dfs & bfs (#70) * feat: v5 algorithm bfs * test: bfs unit test * fix: fix initCallbacks * feat: v5 algorithm dfs * test: dfs unit test * fix: fix lint * feat: add type checking for structs --- __tests__/unit/bfs.spec.ts | 200 ++++++++++++++++++ __tests__/unit/dfs.spec.ts | 183 ++++++++++++++++ package.json | 2 +- packages/graph/src/bfs.ts | 69 ++++++ packages/graph/src/dfs.ts | 52 +++++ packages/graph/src/index.ts | 2 + packages/graph/src/k-core.ts | 6 +- packages/graph/src/structs/linked-list.ts | 242 ++++++++++++++++++++++ packages/graph/src/structs/queue.ts | 43 ++++ packages/graph/src/structs/stack.ts | 62 ++++++ packages/graph/src/types.ts | 11 +- 11 files changed, 866 insertions(+), 6 deletions(-) create mode 100644 __tests__/unit/bfs.spec.ts create mode 100644 __tests__/unit/dfs.spec.ts create mode 100644 packages/graph/src/bfs.ts create mode 100644 packages/graph/src/dfs.ts create mode 100644 packages/graph/src/structs/linked-list.ts create mode 100644 packages/graph/src/structs/queue.ts create mode 100644 packages/graph/src/structs/stack.ts diff --git a/__tests__/unit/bfs.spec.ts b/__tests__/unit/bfs.spec.ts new file mode 100644 index 0000000..baf0a8f --- /dev/null +++ b/__tests__/unit/bfs.spec.ts @@ -0,0 +1,200 @@ +import breadthFirstSearch from "../../packages/graph/src/bfs"; +import { Graph } from "@antv/graphlib"; + +const data = { + nodes: [ + { + id: 'A', + data: {} + }, + { + id: 'B', + data: {} + }, + { + id: 'C', + data: {} + }, + { + id: 'D', + data: {} + }, + { + id: 'E', + data: {} + }, + { + id: 'F', + data: {} + }, + { + id: 'G', + data: {} + }, + ], + edges: [ + { + id: 'e1', + source: 'A', + target: 'B', + data: {} + }, + { + id: 'e2', + source: 'B', + target: 'C', + data: {} + }, + { + id: 'e3', + source: 'C', + target: 'G', + data: {} + }, + { + id: 'e4', + source: 'A', + target: 'D', + data: {} + }, + { + id: 'e5', + source: 'A', + target: 'E', + data: {} + }, + { + id: 'e6', + source: 'E', + target: 'F', + data: {} + }, + { + id: 'e7', + source: 'F', + target: 'D', + data: {} + }, + { + id: 'e8', + source: 'D', + target: 'E', + data: {} + }, + ], +}; +const graph = new Graph(data); + +describe('breadthFirstSearch', () => { + it('should perform BFS operation on graph', () => { + const enterNodeCallback = jest.fn(); + const leaveNodeCallback = jest.fn(); + + // Traverse graphs without callbacks first. + breadthFirstSearch(graph, 'A'); + + // Traverse graph with enterNode and leaveNode callbacks. + breadthFirstSearch(graph, 'A', { + enter: enterNodeCallback, + leave: leaveNodeCallback, + }); + expect(enterNodeCallback).toHaveBeenCalledTimes(graph.getAllNodes().length); + expect(leaveNodeCallback).toHaveBeenCalledTimes(graph.getAllNodes().length); + + const nodeA = 'A'; + const nodeB = 'B'; + const nodeC = 'C'; + const nodeD = 'D'; + const nodeE = 'E'; + const nodeF = 'F'; + const nodeG = 'G'; + + const enterNodeParamsMap = [ + { currentNode: nodeA, previousNode: '' }, + { currentNode: nodeB, previousNode: nodeA }, + { currentNode: nodeD, previousNode: nodeB }, + { currentNode: nodeE, previousNode: nodeD }, + { currentNode: nodeC, previousNode: nodeE }, + { currentNode: nodeF, previousNode: nodeC }, + { currentNode: nodeG, previousNode: nodeF }, + ]; + + for (let callIndex = 0; callIndex < 6; callIndex += 1) { + const params = enterNodeCallback.mock.calls[callIndex][0]; + expect(params.current).toEqual(enterNodeParamsMap[callIndex].currentNode); + expect(params.previous).toEqual( + enterNodeParamsMap[callIndex].previousNode && + enterNodeParamsMap[callIndex].previousNode, + ); + } + + const leaveNodeParamsMap = [ + { currentNode: nodeA, previousNode: '' }, + { currentNode: nodeB, previousNode: nodeA }, + { currentNode: nodeD, previousNode: nodeB }, + { currentNode: nodeE, previousNode: nodeD }, + { currentNode: nodeC, previousNode: nodeE }, + { currentNode: nodeF, previousNode: nodeC }, + { currentNode: nodeG, previousNode: nodeF }, + ]; + + for (let callIndex = 0; callIndex < 6; callIndex += 1) { + const params = leaveNodeCallback.mock.calls[callIndex][0]; + expect(params.current).toEqual(leaveNodeParamsMap[callIndex].currentNode); + expect(params.previous).toEqual( + leaveNodeParamsMap[callIndex].previousNode && + leaveNodeParamsMap[callIndex].previousNode, + ); + } + }); + + it('should allow to create custom node visiting logic', () => { + + const enterNodeCallback = jest.fn(); + const leaveNodeCallback = jest.fn(); + + // Traverse graph with enterNode and leaveNode callbacks. + breadthFirstSearch(graph, 'A', { + enter: enterNodeCallback, + leave: leaveNodeCallback, + allowTraversal: ({ current, next }) => { + return !(current === 'A' && next === 'B'); + }, + }); + + expect(enterNodeCallback).toHaveBeenCalledTimes(4); + expect(leaveNodeCallback).toHaveBeenCalledTimes(4); + + const enterNodeParamsMap = [ + { currentNode: 'A', previousNode: '' }, + { currentNode: 'D', previousNode: 'A' }, + { currentNode: 'E', previousNode: 'D' }, + { currentNode: 'F', previousNode: 'E' }, + { currentNode: 'D', previousNode: 'F' }, + ]; + + for (let callIndex = 0; callIndex < 4; callIndex += 1) { + const params = enterNodeCallback.mock.calls[callIndex][0]; + expect(params.current).toEqual(enterNodeParamsMap[callIndex].currentNode); + expect(params.previous).toEqual( + enterNodeParamsMap[callIndex].previousNode, + ); + } + + const leaveNodeParamsMap = [ + { currentNode: 'A', previousNode: '' }, + { currentNode: 'D', previousNode: 'A' }, + { currentNode: 'E', previousNode: 'D' }, + { currentNode: 'F', previousNode: 'E' }, + { currentNode: 'D', previousNode: 'F' }, + ]; + + for (let callIndex = 0; callIndex < 4; callIndex += 1) { + const params = leaveNodeCallback.mock.calls[callIndex][0]; + expect(params.current).toEqual(leaveNodeParamsMap[callIndex].currentNode); + expect(params.previous).toEqual( + leaveNodeParamsMap[callIndex].previousNode, + ); + } + }); +}); diff --git a/__tests__/unit/dfs.spec.ts b/__tests__/unit/dfs.spec.ts new file mode 100644 index 0000000..aca783c --- /dev/null +++ b/__tests__/unit/dfs.spec.ts @@ -0,0 +1,183 @@ +import depthFirstSearch from "../../packages/graph/src/dfs"; +import { Graph } from "@antv/graphlib"; + + +const data = { + nodes: [ + { + id: 'A', + data: {} + }, + { + id: 'B', + data: {} + }, + { + id: 'C', + data: {} + }, + { + id: 'D', + data: {} + }, + { + id: 'E', + data: {} + }, + { + id: 'F', + data: {} + }, + { + id: 'G', + data: {} + }, + ], + edges: [ + { + id: 'e1', + source: 'A', + target: 'B', + data: {} + }, + { + id: 'e2', + source: 'B', + target: 'C', + data: {} + }, + { + id: 'e3', + source: 'C', + target: 'G', + data: {} + }, + { + id: 'e4', + source: 'A', + target: 'D', + data: {} + }, + { + id: 'e5', + source: 'A', + target: 'E', + data: {} + }, + { + id: 'e6', + source: 'E', + target: 'F', + data: {} + }, + { + id: 'e7', + source: 'F', + target: 'D', + data: {} + }, + { + id: 'e8', + source: 'D', + target: 'E', + data: {} + }, + ], +}; +const graph = new Graph(data); +describe('depthFirstSearch', () => { + it('should perform DFS operation on graph', () => { + + const enterNodeCallback = jest.fn(); + const leaveNodeCallback = jest.fn(); + + // Traverse graphs without callbacks first to check default ones. + depthFirstSearch(graph, 'A'); + + // Traverse graph with enterNode and leaveNode callbacks. + depthFirstSearch(graph, 'A', { + enter: enterNodeCallback, + leave: leaveNodeCallback, + }); + expect(enterNodeCallback).toHaveBeenCalledTimes(graph.getAllNodes().length); + expect(leaveNodeCallback).toHaveBeenCalledTimes(graph.getAllNodes().length); + + const enterNodeParamsMap = [ + { currentNode: 'A', previousNode: '' }, + { currentNode: 'B', previousNode: 'A' }, + { currentNode: 'C', previousNode: 'B' }, + { currentNode: 'G', previousNode: 'C' }, + { currentNode: 'D', previousNode: 'A' }, + { currentNode: 'F', previousNode: 'D' }, + { currentNode: 'E', previousNode: 'F' }, + ]; + for (let callIndex = 0; callIndex < data.nodes.length; callIndex += 1) { + const params = enterNodeCallback.mock.calls[callIndex][0]; + expect(params.previous).toEqual( + enterNodeParamsMap[callIndex].previousNode, + ); + } + + depthFirstSearch(graph, 'B', { + enter: enterNodeCallback, + leave: leaveNodeCallback, + }); + const leaveNodeParamsMap = [ + { currentNode: 'G', previousNode: 'C' }, + { currentNode: 'C', previousNode: 'B' }, + { currentNode: 'B', previousNode: 'A' }, + { currentNode: 'E', previousNode: 'F' }, + { currentNode: 'F', previousNode: 'D' }, + { currentNode: 'D', previousNode: 'A' }, + { currentNode: 'A', previousNode: '' }, + ]; + + for (let callIndex = 0; callIndex < data.nodes.length; callIndex += 1) { + const params = leaveNodeCallback.mock.calls[callIndex][0]; + expect(params.previous).toEqual( + leaveNodeParamsMap[callIndex].previousNode, + ); + } + }); + + it('allow users to redefine node visiting logic', () => { + const enterNodeCallback = jest.fn(); + const leaveNodeCallback = jest.fn(); + depthFirstSearch(graph, 'A', { + enter: enterNodeCallback, + leave: leaveNodeCallback, + allowTraversal: ({ current: currentNode, next: nextNode }) => { + return !(currentNode === 'A' && nextNode === 'B'); + }, + }); + expect(enterNodeCallback).toHaveBeenCalledTimes(4); + expect(leaveNodeCallback).toHaveBeenCalledTimes(4); + + const enterNodeParamsMap = [ + { currentNode: 'A', previousNode: '' }, + { currentNode: 'D', previousNode: 'A' }, + { currentNode: 'F', previousNode: 'D' }, + { currentNode: 'E', previousNode: 'F' }, + ]; + + for (let callIndex = 0; callIndex < 4; callIndex += 1) { + const params = enterNodeCallback.mock.calls[callIndex][0]; + expect(params.previous && params.previous).toEqual( + enterNodeParamsMap[callIndex].previousNode, + ); + } + const leaveNodeParamsMap = [ + { currentNode: 'E', previousNode: 'F' }, + { currentNode: 'F', previousNode: 'D' }, + { currentNode: 'D', previousNode: 'A' }, + { currentNode: 'A', previousNode: '' }, + ]; + for (let callIndex = 0; callIndex < 4; callIndex += 1) { + const params = leaveNodeCallback.mock.calls[callIndex][0]; + expect(params.current).toEqual(leaveNodeParamsMap[callIndex].currentNode); + expect(params.previous).toEqual( + leaveNodeParamsMap[callIndex].previousNode, + ); + } + }); +}); diff --git a/package.json b/package.json index c6cac2c..42043cd 100644 --- a/package.json +++ b/package.json @@ -21,7 +21,7 @@ "build:ci": "pnpm -r run build:ci", "prepare": "husky install", "test": "jest", - "test_one": "jest ./__tests__/unit/kCore.spec.ts", + "test_one": "jest ./__tests__/unit/dfs.spec.ts", "coverage": "jest --coverage", "build:site": "vite build", "deploy": "gh-pages -d site/dist", diff --git a/packages/graph/src/bfs.ts b/packages/graph/src/bfs.ts new file mode 100644 index 0000000..c8f73f6 --- /dev/null +++ b/packages/graph/src/bfs.ts @@ -0,0 +1,69 @@ +import Queue from './structs/queue'; +import { Graph, IAlgorithmCallbacks, NodeID } from './types'; + +/** +* @param startNodeId The ID of the bfs traverse starting node. +* @param callbacks Optional object containing callback functions. + - allowTraversal: Determines if BFS should traverse from the vertex along its edges to its neighbors. By default, a node can only be traversed once. + - enterNode: Called when BFS visits a node. + - leaveNode: Called after BFS visits the node. +*/ +function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks) { + const initiatedCallback = callbacks; + const stubCallback = () => { }; + const allowTraversalCallback = () => true; + initiatedCallback.allowTraversal = callbacks.allowTraversal || allowTraversalCallback; + initiatedCallback.enter = callbacks.enter || stubCallback; + initiatedCallback.leave = callbacks.leave || stubCallback; + return initiatedCallback; +} + +/** +Performs breadth-first search (BFS) traversal on a graph. +@param graph - The graph to perform BFS on. +@param startNodeId - The ID of the starting node for BFS. +@param originalCallbacks - Optional object containing callback functions for BFS. +*/ +const breadthFirstSearch = ( + graph: Graph, + startNodeId: NodeID, + originalCallbacks?: IAlgorithmCallbacks, +) => { + const visit = new Set(); + const callbacks = initCallbacks(originalCallbacks); + const nodeQueue = new Queue(); + // init Queue. Enqueue node ID. + nodeQueue.enqueue(startNodeId); + visit.add(startNodeId); + let previousNodeId: NodeID = ''; + // 遍历队列中的所有顶点 + while (!nodeQueue.isEmpty()) { + const currentNodeId: NodeID = nodeQueue.dequeue(); + callbacks.enter({ + current: currentNodeId, + previous: previousNodeId, + }); + + // Enqueue all neighbors of currentNode + graph.getNeighbors(currentNodeId).forEach((nextNode) => { + const nextNodeId = nextNode.id; + if ( + callbacks.allowTraversal({ + previous: previousNodeId, + current: currentNodeId, + next: nextNodeId, + }) && !visit.has(nextNodeId) + ) { + visit.add(nextNodeId); + nodeQueue.enqueue(nextNodeId); + } + }); + callbacks.leave({ + current: currentNodeId, + previous: previousNodeId, + }); + previousNodeId = currentNodeId; + } +}; + +export default breadthFirstSearch; diff --git a/packages/graph/src/dfs.ts b/packages/graph/src/dfs.ts new file mode 100644 index 0000000..83a131f --- /dev/null +++ b/packages/graph/src/dfs.ts @@ -0,0 +1,52 @@ +import { Graph, IAlgorithmCallbacks, NodeID } from './types'; + +function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks) { + const initiatedCallback = callbacks; + const stubCallback = () => { }; + const allowTraversalCallback = () => true; + initiatedCallback.allowTraversal = callbacks.allowTraversal || allowTraversalCallback; + initiatedCallback.enter = callbacks.enter || stubCallback; + initiatedCallback.leave = callbacks.leave || stubCallback; + + return initiatedCallback; +} + +function depthFirstSearchRecursive( + graph: Graph, + currentNodeId: NodeID, + previousNodeId: NodeID, + callbacks: IAlgorithmCallbacks, + visit: Set +) { + callbacks.enter({ + current: currentNodeId, + previous: previousNodeId, + }); + graph.getNeighbors(currentNodeId).forEach((nextNode) => { + const nextNodeId = nextNode.id; + if ( + callbacks.allowTraversal({ + previous: previousNodeId, + current: currentNodeId, + next: nextNodeId, + }) && !visit.has(nextNodeId) + ) { + visit.add(nextNodeId); + depthFirstSearchRecursive(graph, nextNodeId, currentNodeId, callbacks, visit); + } + }); + callbacks.leave({ + current: currentNodeId, + previous: previousNodeId, + }); +} + +export default function depthFirstSearch( + graph: Graph, + startNodeId: NodeID, + originalCallbacks?: IAlgorithmCallbacks, +) { + const visit = new Set(); + visit.add(startNodeId); + depthFirstSearchRecursive(graph, startNodeId, '', initCallbacks(originalCallbacks), visit); +} diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index 6a22580..772f5dc 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -4,3 +4,5 @@ export * from "./louvain"; export * from "./iLouvain"; export * from "./k-core"; export * from "./floydWarshall"; +export * from "./bfs"; +export * from "./dfs"; \ No newline at end of file diff --git a/packages/graph/src/k-core.ts b/packages/graph/src/k-core.ts index b2a2dd6..9d8f8de 100644 --- a/packages/graph/src/k-core.ts +++ b/packages/graph/src/k-core.ts @@ -12,12 +12,12 @@ export function kCore( const nodes = graph.getAllNodes(); let edges = graph.getAllEdges(); nodes.sort((a, b) => graph.getDegree(a.id, 'both') - graph.getDegree(b.id, 'both')); - let i = 0; + const i = 0; while (true) { const curNode = nodes[i]; if (graph.getDegree(curNode.id, 'both') >= k) break; - nodes.splice(i, 1);//remove node - edges = edges.filter(e => !(e.source === i || e.target === i)); + nodes.splice(i, 1);// remove node + edges = edges.filter((e) => !(e.source === i || e.target === i)); } return { nodes, edges }; } \ No newline at end of file diff --git a/packages/graph/src/structs/linked-list.ts b/packages/graph/src/structs/linked-list.ts new file mode 100644 index 0000000..89abeee --- /dev/null +++ b/packages/graph/src/structs/linked-list.ts @@ -0,0 +1,242 @@ + +/** + * ListNode in LinkedList + */ +export class LinkedListNode { + public value: T; + + public next: LinkedListNode; + + constructor(value: T, next: LinkedListNode = null) { + this.value = value; + this.next = next; + } + + toString(callback?: Function) { + return callback ? callback(this.value) : `${this.value}`; + } +} + +export default class LinkedList { + public head: LinkedListNode; + + public tail: LinkedListNode; + + public compare: Function; + defaultComparator = (a: T, b: T) => { + if (a === b) { + return true; + } + return false; + }; + + constructor(comparator?: Function) { + this.head = null; + this.tail = null; + this.compare = comparator || this.defaultComparator; + } + + /** + * Adds the specified element to the header of the linked list + * @param value The element + */ + prepend(value: T) { + // 在头部添加一个节点 + const newNode = new LinkedListNode(value, this.head); + this.head = newNode; + + if (!this.tail) { + this.tail = newNode; + } + + return this; + } + + /** + * Adds the specified element to the linked list + * @param value The element + */ + append(value: T) { + const newNode = new LinkedListNode(value); + + // 如果不存在头节点，则将创建的新节点作为头节点 + if (!this.head) { + this.head = newNode; + this.tail = newNode; + + return this; + } + + // 将新节点附加到链表末尾 + this.tail.next = newNode; + this.tail = newNode; + + return this; + } + + /** + * Delete the specified element + * @param value The element + */ + delete(value: T): LinkedListNode { + if (!this.head) { + return null; + } + + let deleteNode = null; + + // 如果删除的是头部元素，则将next作为头元素 + while (this.head && this.compare(this.head.value, value)) { + deleteNode = this.head; + this.head = this.head.next; + } + + let currentNode = this.head; + + if (currentNode !== null) { + // 如果删除了节点以后，将next节点前移 + while (currentNode.next) { + if (this.compare(currentNode.next.value, value)) { + deleteNode = currentNode.next; + currentNode.next = currentNode.next.next; + } else { + currentNode = currentNode.next; + } + } + } + + // 检查尾部节点是否被删除 + if (this.compare(this.tail.value, value)) { + this.tail = currentNode; + } + + return deleteNode; + } + + /** + * Finds the first occurrence of a node in the linked list that matches the specified value or satisfies the callback function. + @param value - The value to search for in the linked list. + @param callback - An optional callback function to determine if a node matches the search criteria. + Copy.The callback should accept a value from a node as its argument and return a boolean indicating a match. + @returns The first LinkedListNode that matches the search criteria, or null if no match is found. + */ + find({ value = undefined, callback = undefined }: { value: T, callback: Function }): LinkedListNode { + if (!this.head) { + return null; + } + let currentNode = this.head; + while (currentNode) { + //find by callback first + if (callback && callback(currentNode.value)) { + return currentNode; + } + if (value !== undefined && this.compare(currentNode.value, value)) { + return currentNode; + } + currentNode = currentNode.next; + } + + return null; + } + + /** + * Delete tail node + */ + deleteTail() { + const deletedTail = this.tail; + + if (this.head === this.tail) { + // 链表中只有一个元素 + this.head = null; + this.tail = null; + return deletedTail; + } + + let currentNode = this.head; + while (currentNode.next) { + if (!currentNode.next.next) { + currentNode.next = null; + } else { + currentNode = currentNode.next; + } + } + + this.tail = currentNode; + + return deletedTail; + } + + /** + * Delete head node + */ + deleteHead() { + if (!this.head) { + return null; + } + + const deletedHead = this.head; + + if (this.head.next) { + this.head = this.head.next; + } else { + this.head = null; + this.tail = null; + } + + return deletedHead; + } + + /** + * Convert a set of elements to nodes in a linked list + * @param values element in linkedlist + */ + fromArray(values: T[]) { + values.forEach((value) => this.append(value)); + return this; + } + + /** + * Convert nodes in a linked list into array elements + */ + toArray() { + const nodes = []; + + let currentNode = this.head; + + while (currentNode) { + nodes.push(currentNode); + currentNode = currentNode.next; + } + + return nodes; + } + + /** + * Invert element nodes in a linked list + */ + reverse() { + let currentNode = this.head; + let prevNode = null; + let nextNode = null; + while (currentNode) { + // 存储下一个元素节点 + nextNode = currentNode.next; + + // 更改当前节点的下一个节点，以便将它连接到上一个节点上 + currentNode.next = prevNode; + + // 将 prevNode 和 currentNode 向前移动一步 + prevNode = currentNode; + currentNode = nextNode; + } + + this.tail = this.head; + this.head = prevNode; + } + + toString(callback: Function = undefined) { + return this.toArray() + .map((node) => node.toString(callback)) + .toString(); + } +} diff --git a/packages/graph/src/structs/queue.ts b/packages/graph/src/structs/queue.ts new file mode 100644 index 0000000..3c5b8c5 --- /dev/null +++ b/packages/graph/src/structs/queue.ts @@ -0,0 +1,43 @@ +import LinkedList from './linked-list'; + +export default class Queue { + public linkedList: LinkedList; + + constructor() { + this.linkedList = new LinkedList(); + } + + public isEmpty() { + return !this.linkedList.head; + } + + /** + * get the first element without dequeue + */ + public peek() { + if (!this.linkedList.head) { + return null; + } + return this.linkedList.head.value; + } + + /** + * enqueue an element at the tail + * @param value + */ + public enqueue(value: T) { + this.linkedList.append(value); + } + + /** + * Dequeue the first element. If the queue is empty, return null. + */ + public dequeue() { + const removeHead = this.linkedList.deleteHead(); + return removeHead ? removeHead.value : null; + } + + public toString(callback?: any) { + return this.linkedList.toString(callback); + } +} diff --git a/packages/graph/src/structs/stack.ts b/packages/graph/src/structs/stack.ts new file mode 100644 index 0000000..37336d1 --- /dev/null +++ b/packages/graph/src/structs/stack.ts @@ -0,0 +1,62 @@ +import LinkedList from './linked-list'; +export default class Stack { + + private linkedList: LinkedList; + + private maxStep: number; + + constructor(maxStep: number = 10) { + this.linkedList = new LinkedList(); + this.maxStep = maxStep; + } + + get length() { + return this.linkedList.toArray().length; + } + + /** + * Determine whether the stack is empty, if there is no header element in the linked list, the stack is empty + */ + isEmpty() { + return !this.linkedList.head; + } + + /** + * Whether to the maximum length of the defined stack, if the maximum length is reached, the stack is no longer allowed to enter the stack + */ + isMaxStack() { + return this.toArray().length >= this.maxStep; + } + + /** + * Access the top element + */ + peek() { + if (this.isEmpty()) { + return null; + } + return this.linkedList.head.value; + } + + push(value: T) { + this.linkedList.prepend(value); + if (this.length > this.maxStep) { + this.linkedList.deleteTail(); + } + } + + pop() { + const removeHead = this.linkedList.deleteHead(); + return removeHead ? removeHead.value : null; + } + + toArray() { + return this.linkedList.toArray().map((node) => node.value); + } + + clear() { + while (!this.isEmpty()) { + this.pop(); + } + } +} diff --git a/packages/graph/src/types.ts b/packages/graph/src/types.ts index 8fe9dce..fb9038f 100644 --- a/packages/graph/src/types.ts +++ b/packages/graph/src/types.ts @@ -2,7 +2,7 @@ import { Edge, Graph as IGraph, Node, PlainObject } from "@antv/graphlib"; // 数据集中属性/特征值分布的map export interface KeyValueMap { - [key:string]: any[]; + [key: string]: any[]; } export interface NodeData extends PlainObject { @@ -30,4 +30,11 @@ export interface ClusterMap { export type Graph = IGraph; -export type Matrix = number[]; \ No newline at end of file +export type Matrix = number[]; +export interface IAlgorithmCallbacks { + enter?: (param: { current: NodeID; previous: NodeID }) => void; + leave?: (param: { current: NodeID; previous?: NodeID }) => void; + allowTraversal?: (param: { previous?: NodeID; current?: NodeID; next: NodeID }) => boolean; +} + +export type NodeID = string | number; \ No newline at end of file From 99200544fd450c134ff4ab0e384ce86d8248e9bf Mon Sep 17 00:00:00 2001 From: Cee Date: Tue, 12 Sep 2023 09:18:34 +0800 Subject: [PATCH 06/15] feat: add cosine similarity algorithm (#69) * feat: cosine similarity algorithm * test: unit test * fix: change default export to export * feat: v5 algorithm nodes-cosine-similarity * test: nodes cossim unit test * fix: fix import bug, fix: change NodeSimilarity type * fix: remove annotation --- __tests__/unit/bfs.spec.ts | 2 +- __tests__/unit/cosine-similarity.spec.ts | 41 +++++++ __tests__/unit/dfs.spec.ts | 2 +- .../unit/nodes-cosine-similarity.spec.ts | 109 ++++++++++++++++++ package.json | 2 +- packages/graph/src/bfs.ts | 4 +- packages/graph/src/cosine-similarity.ts | 27 +++++ packages/graph/src/dfs.ts | 2 +- packages/graph/src/index.ts | 4 +- packages/graph/src/nodes-cosine-similarity.ts | 43 +++++++ packages/graph/src/types.ts | 8 +- packages/graph/src/utils.ts | 4 +- tsconfig.json | 3 +- 13 files changed, 239 insertions(+), 12 deletions(-) create mode 100644 __tests__/unit/cosine-similarity.spec.ts create mode 100644 __tests__/unit/nodes-cosine-similarity.spec.ts create mode 100644 packages/graph/src/cosine-similarity.ts create mode 100644 packages/graph/src/nodes-cosine-similarity.ts diff --git a/__tests__/unit/bfs.spec.ts b/__tests__/unit/bfs.spec.ts index baf0a8f..35ad081 100644 --- a/__tests__/unit/bfs.spec.ts +++ b/__tests__/unit/bfs.spec.ts @@ -1,4 +1,4 @@ -import breadthFirstSearch from "../../packages/graph/src/bfs"; +import { breadthFirstSearch } from "../../packages/graph/src"; import { Graph } from "@antv/graphlib"; const data = { diff --git a/__tests__/unit/cosine-similarity.spec.ts b/__tests__/unit/cosine-similarity.spec.ts new file mode 100644 index 0000000..fe595e1 --- /dev/null +++ b/__tests__/unit/cosine-similarity.spec.ts @@ -0,0 +1,41 @@ +import { cosineSimilarity } from "../../packages/graph/src"; + +describe('cosineSimilarity abnormal demo: ', () => { + it('item contains only zeros: ', () => { + const item = [0, 0, 0]; + const targetTtem = [3, 1, 1]; + const cosineSimilarityValue = cosineSimilarity(item, targetTtem); + expect(cosineSimilarityValue).toBe(0); + }); + it('targetTtem contains only zeros: ', () => { + const item = [3, 5, 2]; + const targetTtem = [0, 0, 0]; + const cosineSimilarityValue = cosineSimilarity(item, targetTtem); + expect(cosineSimilarityValue).toBe(0); + }); + it('item and targetTtem both contains only zeros: ', () => { + const item = [0, 0, 0]; + const targetTtem = [0, 0, 0]; + const cosineSimilarityValue = cosineSimilarity(item, targetTtem); + expect(cosineSimilarityValue).toBe(0); + }); +}); + +describe('cosineSimilarity normal demo: ', () => { + it('demo similar: ', () => { + const item = [30, 0, 100]; + const targetTtem = [32, 1, 120]; + const cosineSimilarityValue = cosineSimilarity(item, targetTtem); + expect(cosineSimilarityValue).toBeGreaterThanOrEqual(0); + expect(cosineSimilarityValue).toBeLessThan(1); + expect(Number(cosineSimilarityValue.toFixed(3))).toBe(0.999); + }); + it('demo dissimilar: ', () => { + const item = [10, 300, 2]; + const targetTtem = [1, 2, 30]; + const cosineSimilarityValue = cosineSimilarity(item, targetTtem); + expect(cosineSimilarityValue).toBeGreaterThanOrEqual(0); + expect(cosineSimilarityValue).toBeLessThan(1); + expect(Number(cosineSimilarityValue.toFixed(3))).toBe(0.074); + }); +}); diff --git a/__tests__/unit/dfs.spec.ts b/__tests__/unit/dfs.spec.ts index aca783c..fb1e5d7 100644 --- a/__tests__/unit/dfs.spec.ts +++ b/__tests__/unit/dfs.spec.ts @@ -1,4 +1,4 @@ -import depthFirstSearch from "../../packages/graph/src/dfs"; +import { depthFirstSearch } from "../../packages/graph/src"; import { Graph } from "@antv/graphlib"; diff --git a/__tests__/unit/nodes-cosine-similarity.spec.ts b/__tests__/unit/nodes-cosine-similarity.spec.ts new file mode 100644 index 0000000..e0e0246 --- /dev/null +++ b/__tests__/unit/nodes-cosine-similarity.spec.ts @@ -0,0 +1,109 @@ +import { nodesCosineSimilarity } from "../../packages/graph/src"; +import propertiesGraphData from '../data/cluster-origin-properties-data.json'; +import { NodeSimilarity } from "../../packages/graph/src/types"; + +describe('nodesCosineSimilarity abnormal demo', () => { + it('no properties demo: ', () => { + const nodes = [ + { + id: 'node-0', + data: {}, + }, + { + id: 'node-1', + data: {}, + }, + { + id: 'node-2', + data: {}, + }, + { + id: 'node-3', + data: {}, + } + ]; + const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeSimilarity[], nodes[0]); + expect(allCosineSimilarity.length).toBe(3); + expect(similarNodes.length).toBe(3); + expect(allCosineSimilarity[0]).toBe(0); + expect(allCosineSimilarity[1]).toBe(0); + expect(allCosineSimilarity[2]).toBe(0); + }); +}); + + +describe('nodesCosineSimilarity normal demo', () => { + it('simple demo: ', () => { + const nodes = [ + { + id: 'node-0', + data: { + amount: 10, + } + }, + { + id: 'node-2', + data: { + amount: 100, + } + }, + { + id: 'node-3', + data: { + amount: 1000, + } + }, + { + id: 'node-4', + data: { + amount: 50, + } + } + ]; + const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeSimilarity[], nodes[0], ['amount']); + expect(allCosineSimilarity.length).toBe(3); + expect(similarNodes.length).toBe(3); + allCosineSimilarity.forEach(data => { + expect(data).toBeGreaterThanOrEqual(0); + expect(data).toBeLessThanOrEqual(1); + }) + }); + + it('complex demo: ', () => { + const { nodes } = propertiesGraphData; + const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeSimilarity[], nodes[16]); + expect(allCosineSimilarity.length).toBe(16); + expect(similarNodes.length).toBe(16); + allCosineSimilarity.forEach(data => { + expect(data).toBeGreaterThanOrEqual(0); + expect(data).toBeLessThanOrEqual(1); + }) + }); + + + it('demo use involvedKeys: ', () => { + const involvedKeys = ['amount', 'wifi']; + const { nodes } = propertiesGraphData; + const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeSimilarity[], nodes[16], involvedKeys); + expect(allCosineSimilarity.length).toBe(16); + expect(similarNodes.length).toBe(16); + allCosineSimilarity.forEach(data => { + expect(data).toBeGreaterThanOrEqual(0); + expect(data).toBeLessThanOrEqual(1); + }) + expect(similarNodes[0].id).toBe('node-11'); + }); + + it('demo use uninvolvedKeys: ', () => { + const uninvolvedKeys = ['amount']; + const { nodes } = propertiesGraphData; + const { allCosineSimilarity, similarNodes } = nodesCosineSimilarity(nodes as NodeSimilarity[], nodes[16], [], uninvolvedKeys); + expect(allCosineSimilarity.length).toBe(16); + expect(similarNodes.length).toBe(16); + allCosineSimilarity.forEach(data => { + expect(data).toBeGreaterThanOrEqual(0); + expect(data).toBeLessThanOrEqual(1); + }) + expect(similarNodes[0].id).toBe('node-11'); + }); +}); diff --git a/package.json b/package.json index 42043cd..8a6e34a 100644 --- a/package.json +++ b/package.json @@ -21,7 +21,7 @@ "build:ci": "pnpm -r run build:ci", "prepare": "husky install", "test": "jest", - "test_one": "jest ./__tests__/unit/dfs.spec.ts", + "test_one": "jest ./__tests__/unit/nodes-cosine-similarity.spec.ts", "coverage": "jest --coverage", "build:site": "vite build", "deploy": "gh-pages -d site/dist", diff --git a/packages/graph/src/bfs.ts b/packages/graph/src/bfs.ts index c8f73f6..209b8e1 100644 --- a/packages/graph/src/bfs.ts +++ b/packages/graph/src/bfs.ts @@ -24,7 +24,7 @@ Performs breadth-first search (BFS) traversal on a graph. @param startNodeId - The ID of the starting node for BFS. @param originalCallbacks - Optional object containing callback functions for BFS. */ -const breadthFirstSearch = ( +export const breadthFirstSearch = ( graph: Graph, startNodeId: NodeID, originalCallbacks?: IAlgorithmCallbacks, @@ -65,5 +65,3 @@ const breadthFirstSearch = ( previousNodeId = currentNodeId; } }; - -export default breadthFirstSearch; diff --git a/packages/graph/src/cosine-similarity.ts b/packages/graph/src/cosine-similarity.ts new file mode 100644 index 0000000..554623b --- /dev/null +++ b/packages/graph/src/cosine-similarity.ts @@ -0,0 +1,27 @@ +import { Vector } from "./vector"; + +/** +Calculates the cosine similarity +@param item - The element. +@param targetItem - The target element. +@returns The cosine similarity between the item and the targetItem. +*/ +export const cosineSimilarity = ( + item: number[], + targetItem: number[], +): number => { + // Vector of the target element + const targetItemVector = new Vector(targetItem); + // Norm of the target element vector + const targetNodeNorm2 = targetItemVector.norm2(); + // Vector of the item + const itemVector = new Vector(item); + // Norm of the item vector + const itemNorm2 = itemVector.norm2(); + // Calculate the dot product of the item vector and the target element vector + const dot = targetItemVector.dot(itemVector); + const norm2Product = targetNodeNorm2 * itemNorm2; + // Calculate the cosine similarity between the item vector and the target element vector + const cosineSimilarity = norm2Product ? dot / norm2Product : 0; + return cosineSimilarity; +} diff --git a/packages/graph/src/dfs.ts b/packages/graph/src/dfs.ts index 83a131f..f66be8a 100644 --- a/packages/graph/src/dfs.ts +++ b/packages/graph/src/dfs.ts @@ -41,7 +41,7 @@ function depthFirstSearchRecursive( }); } -export default function depthFirstSearch( +export function depthFirstSearch( graph: Graph, startNodeId: NodeID, originalCallbacks?: IAlgorithmCallbacks, diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index 772f5dc..6af8072 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -5,4 +5,6 @@ export * from "./iLouvain"; export * from "./k-core"; export * from "./floydWarshall"; export * from "./bfs"; -export * from "./dfs"; \ No newline at end of file +export * from "./dfs"; +export * from "./cosine-similarity" +export * from "./nodes-cosine-similarity"; \ No newline at end of file diff --git a/packages/graph/src/nodes-cosine-similarity.ts b/packages/graph/src/nodes-cosine-similarity.ts new file mode 100644 index 0000000..9ef5201 --- /dev/null +++ b/packages/graph/src/nodes-cosine-similarity.ts @@ -0,0 +1,43 @@ +import { clone } from '@antv/util'; +import { getAllProperties, oneHot } from './utils'; +import { NodeSimilarity } from './types'; +import { cosineSimilarity } from './cosine-similarity'; + +/** +Calculates the cosine similarity based on node attributes using the nodes-cosine-similarity algorithm. +This algorithm is used to find similar nodes based on a seed node in a graph. +@param nodes - The data of graph nodes. +@param seedNode - The seed node for similarity calculation. +@param involvedKeys - The collection of keys that are involved in the calculation. +@param uninvolvedKeys - The collection of keys that are not involved in the calculation. +@returns An array of nodes that are similar to the seed node based on cosine similarity. +*/ +export const nodesCosineSimilarity = ( + nodes: NodeSimilarity[] = [], + seedNode: NodeSimilarity, + involvedKeys: string[] = [], + uninvolvedKeys: string[] = [], +): { + allCosineSimilarity: number[], + similarNodes: NodeSimilarity[], +} => { + const similarNodes = clone(nodes.filter(node => node.id !== seedNode.id)); + const seedNodeIndex = nodes.findIndex(node => node.id === seedNode.id); + // Collection of all node properties + const properties = getAllProperties(nodes); + // One-hot feature vectors for all node properties + const allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys) as number[][]; + // Seed node properties + const seedNodeProperties = allPropertiesWeight[seedNodeIndex]; + const allCosineSimilarity: number[] = []; + similarNodes.forEach((node: NodeSimilarity, index: number) => { + const nodeProperties = allPropertiesWeight[index]; + // Calculate the cosine similarity between node vector and seed node vector + const cosineSimilarityValue = cosineSimilarity(nodeProperties, seedNodeProperties); + allCosineSimilarity.push(cosineSimilarityValue); + node.data.cosineSimilarity = cosineSimilarityValue; + }); + // Sort the returned nodes according to cosine similarity + similarNodes.sort((a: NodeSimilarity, b: NodeSimilarity) => b.data.cosineSimilarity - a.data.cosineSimilarity); + return { allCosineSimilarity, similarNodes }; +} diff --git a/packages/graph/src/types.ts b/packages/graph/src/types.ts index fb9038f..d05b75f 100644 --- a/packages/graph/src/types.ts +++ b/packages/graph/src/types.ts @@ -37,4 +37,10 @@ export interface IAlgorithmCallbacks { allowTraversal?: (param: { previous?: NodeID; current?: NodeID; next: NodeID }) => boolean; } -export type NodeID = string | number; \ No newline at end of file +export type NodeID = string | number; + +export type NodeSimilarity = Node & { + data: { + cosineSimilarity?: number; + } +} \ No newline at end of file diff --git a/packages/graph/src/utils.ts b/packages/graph/src/utils.ts index 80470f3..78d5103 100644 --- a/packages/graph/src/utils.ts +++ b/packages/graph/src/utils.ts @@ -50,7 +50,7 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol // 获取所有的属性/特征值 const allValue = Object.values(allKeyValueMap); // 是否所有属性/特征的值都是数值型 - const isAllNumber = allValue.every((value) => value.every((item) => (typeof(item) === 'number'))); + const isAllNumber = allValue.every((value) => value.every((item) => (typeof (item) === 'number'))); // 对数据进行one-hot编码 dataList.forEach((data, index) => { @@ -65,7 +65,7 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol subCode.push(keyValue); } else { // 进行one-hot编码 - for(let i = 0; i < allKeyValue.length; i++) { + for (let i = 0; i < allKeyValue.length; i++) { if (i === valueIndex) { subCode.push(1); } else { diff --git a/tsconfig.json b/tsconfig.json index cf11009..a75eef5 100644 --- a/tsconfig.json +++ b/tsconfig.json @@ -7,6 +7,7 @@ "allowJs": true, "moduleResolution": "node", "resolveJsonModule": true, - "allowSyntheticDefaultImports": true + "allowSyntheticDefaultImports": true, + "esModuleInterop": true, } } From b28d6676434621cd1d92e5bcc4055c6cf7c2fa21 Mon Sep 17 00:00:00 2001 From: Yanyan Wang Date: Tue, 12 Sep 2023 09:53:00 +0800 Subject: [PATCH 07/15] feat: gaddi pattern match cpu version (#71) * feat: gaddi cpu version * chore: lint --- __tests__/data/gaddi-test-data.ts | 58513 ++++++++++++++++++++ __tests__/unit/gaddi.spec.ts | 2440 + packages/graph/src/gSpan/gSpan.ts | 982 + packages/graph/src/gSpan/struct.ts | 120 + packages/graph/src/gaddi.ts | 1444 + packages/graph/src/index.ts | 21 +- packages/graph/src/structs/linked-list.ts | 4 +- packages/graph/src/types.ts | 20 +- 8 files changed, 63528 insertions(+), 16 deletions(-) create mode 100644 __tests__/data/gaddi-test-data.ts create mode 100644 __tests__/unit/gaddi.spec.ts create mode 100644 packages/graph/src/gSpan/gSpan.ts create mode 100644 packages/graph/src/gSpan/struct.ts create mode 100644 packages/graph/src/gaddi.ts diff --git a/__tests__/data/gaddi-test-data.ts b/__tests__/data/gaddi-test-data.ts new file mode 100644 index 0000000..6e171f6 --- /dev/null +++ b/__tests__/data/gaddi-test-data.ts @@ -0,0 +1,58513 @@ +export const nodes20 = { + nodes: [ + { + id: '5', + data: { + dataType: 'Person', + }, + }, + { + id: '4', + data: { + dataType: 'Person', + }, + }, + { + id: '6', + data: { + dataType: 'Person', + }, + }, + { + id: '7', + data: { + dataType: 'Person', + }, + }, + { + id: '1004', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '1005', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '8', + data: { + dataType: 'Person', + }, + }, + { + id: '1006', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '9', + data: { + dataType: 'Person', + }, + }, + { + id: '1007', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '10', + data: { + dataType: 'Person', + }, + }, + { + id: '1008', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '11', + data: { + dataType: 'Person', + }, + }, + { + id: '1009', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '2023', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '1003', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '2024', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '2025', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '2026', + data: { + dataType: 'Enterprise', + }, + }, + { + id: '2027', + data: { + dataType: 'Enterprise', + }, + }, + ], + edges: [ + { + id: 'edge-0.5645474961762444', + source: '4', + target: '5', + data: { + dataType: 'Person2Person#Social', + }, + }, + { + id: 'edge-0.47819899936752686', + source: '4', + target: '6', + data: { + dataType: 'Person2Person#Social', + }, + }, + { + id: 'edge-0.7066997727509052', + source: '4', + target: '7', + data: { + dataType: 'Person2Person#Social', + }, + }, + { + id: 'edge-0.7086220364606017', + source: '4', + target: '5', + data: { + dataType: 'Person2Person#Social', + }, + }, + { + id: 'edge-0.7286109193020414', + source: '4', + target: '6', + data: { + dataType: 'Person2Person#Social', + }, + }, + { + id: 'edge-0.5437848236262293', + source: '4', + target: '7', + data: { + dataType: 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+ }, + { + id: 'edge-0.002762373879622526', + source: '1005', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.19227429325872225', + source: '1005', + target: '2025', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.9612419342702261', + source: '1005', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.8341199246416504', + source: '1005', + target: '2025', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.6288924829061195', + source: '1005', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.18965187947247286', + source: '1005', + target: '2025', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.08413356821177365', + source: '1005', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.9498213334658108', + source: '1005', + target: '2025', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.33414717909979186', + source: '1005', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.8805846309996568', + source: '1005', + target: '2025', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.10051735367698433', + source: '1005', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.7687592786304247', + source: '1005', + target: '2025', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.05084452798952577', + source: '1005', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.034086913541178765', + source: '1005', + target: '2025', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.2561635096465298', + source: '1005', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.9797229412456467', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.8267868053897065', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.4483468959253649', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.21719474669376293', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.8540442139742441', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.9126887059606337', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Guarantee', + }, + }, + { + id: 'edge-0.0063867804817197005', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.24672935492472625', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.03029742524582657', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.7053908763852426', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.6533458326377453', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.9341906294917453', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Investment', + }, + }, + { + id: 'edge-0.7083187090225502', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.3384578287773581', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.4365566429681782', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.025625139431694866', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.09484010238195939', + source: '1006', + target: '2026', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + { + id: 'edge-0.4289782583248687', + source: '1006', + target: '2027', + data: { + dataType: 'Enterprise2Enterprise#Level', + }, + }, + ], +}; +export const nodes77 = { + nodes: [ + { + id: '0', + data: { + cluster: 'A', + label: '0-A', + }, + }, + { + id: '1', + data: { + cluster: 'B', + label: '1-B', + }, + }, + { + id: '2', + data: { + cluster: 'C', + label: '2-C', + }, + }, + { + id: '3', + data: { + cluster: 'C', + label: '3-C', + }, + }, + { + id: '4', + data: { + cluster: 'C', + label: '4-C', + }, + }, + { + id: '5', + data: { + cluster: 'B', + label: '5-B', + }, + }, + { + id: '6', + data: { + cluster: 'C', + label: '6-C', + }, + }, + { + id: '7', + data: { + cluster: 'B', + label: '7-B', + }, + }, + { + id: '8', + data: { + cluster: 'A', + label: '8-A', + }, + }, + { + id: '9', + data: { + cluster: 'A', + label: '9-A', + }, + }, + { + id: '10', + data: { + cluster: 'B', + label: '10-B', + }, + }, + { + id: '11', + data: { + cluster: 'B', + label: '11-B', + }, + }, + { + id: '12', + data: { + cluster: 'C', + label: '12-C', + }, + }, + { + id: '13', + data: { + cluster: 'B', + label: '13-B', + }, + }, + { + id: '14', + data: { + cluster: 'B', + label: '14-B', + }, + }, + { + id: '15', + data: { + cluster: 'B', + label: '15-B', + }, + }, + { + id: '16', + data: { + cluster: 'B', + label: '16-B', + }, + }, + { + id: '17', + data: { + cluster: 'B', + label: '17-B', + }, + }, + { + id: '18', + data: { + cluster: 'B', + label: '18-B', + }, + }, + { + id: '19', + data: { + cluster: 'C', + label: '19-C', + }, + }, + { + id: '20', + data: { + cluster: 'B', + label: '20-B', + }, + }, + { + id: '21', + data: { + cluster: 'A', + label: '21-A', + }, + }, + { + id: '22', + data: { + cluster: 'B', + label: '22-B', + }, + }, + { + id: '23', + data: { + cluster: 'B', + label: '23-B', + }, + }, + { + id: '24', + data: { + cluster: 'C', + label: '24-C', + }, + }, + { + id: '25', + data: { + cluster: 'C', + label: '25-C', + }, + }, + { + id: '26', + data: { + cluster: 'B', + label: '26-B', + }, + }, + { + id: '27', + data: { + cluster: 'A', + label: '27-A', + }, + }, + { + id: '28', + data: { + cluster: 'C', + label: '28-C', + }, + }, + { + id: '29', + data: { + cluster: 'C', + label: '29-C', + }, + }, + { + id: '30', + data: { + cluster: 'B', + label: '30-B', + }, + }, + { + id: '31', + data: { + cluster: 'B', + label: '31-B', + }, + }, + { + id: '32', + data: { + cluster: 'C', + label: '32-C', + }, + }, + { + id: '33', + data: { + cluster: 'A', + label: '33-A', + }, + }, + { + id: '34', + data: { + cluster: 'A', + label: '34-A', + }, + }, + { + id: '35', + data: { + cluster: 'C', + label: '35-C', + }, + }, + { + id: '36', + data: { + cluster: 'A', + label: '36-A', + }, + }, + { + id: '37', + data: { + cluster: 'C', + label: '37-C', + }, + }, + { + id: '38', + data: { + cluster: 'C', + label: '38-C', + }, + }, + { + id: '39', + data: { + cluster: 'B', + label: '39-B', + }, + }, + { + id: '40', + data: { + cluster: 'C', + label: '40-C', + }, + }, + { + id: '41', + data: { + cluster: 'B', + label: '41-B', + }, + }, + { + id: '42', + data: { + cluster: 'B', + label: '42-B', + }, + }, + { + id: '43', + data: { + cluster: 'A', + label: '43-A', + }, + }, + { + id: '44', + data: { + cluster: 'C', + label: '44-C', + }, + }, + { + id: '45', + data: { + cluster: 'B', + label: '45-B', + }, + }, + { + id: '46', + data: { + cluster: 'B', + label: '46-B', + }, + }, + { + id: '47', + data: { + cluster: 'C', + label: '47-C', + }, + }, + { + id: '48', + data: { + cluster: 'B', + label: '48-B', + }, + }, + { + id: '49', + data: { + cluster: 'C', + label: '49-C', + }, + }, + { + id: '50', + data: { + cluster: 'A', + label: '50-A', + }, + }, + { + id: '51', + data: { + cluster: 'C', + label: '51-C', + }, + }, + { + id: '52', + data: { + cluster: 'C', + label: '52-C', + }, + }, + { + id: '53', + data: { + cluster: 'C', + label: '53-C', + }, + }, + { + id: '54', + data: { + cluster: 'C', + label: '54-C', + }, + }, + { + id: '55', + data: { + cluster: 'A', + label: '55-A', + }, + }, + { + id: '56', + data: { + cluster: 'B', + label: '56-B', + }, + }, + { + id: '57', + data: { + cluster: 'A', + label: '57-A', + }, + }, + { + id: '58', + data: { + cluster: 'B', + label: '58-B', + }, + }, + { + id: '59', + data: { + cluster: 'C', + label: '59-C', + }, + }, + { + id: '60', + data: { + cluster: 'B', + label: '60-B', + }, + }, + { + id: '61', + data: { + cluster: 'B', + label: '61-B', + }, + }, + { + id: '62', + data: { + cluster: 'C', + label: '62-C', + }, + }, + { + id: '63', + data: { + cluster: 'B', + label: '63-B', + }, + }, + { + id: '64', + data: { + cluster: 'B', + label: '64-B', + }, + }, + { + id: '65', + data: { + cluster: 'A', + label: '65-A', + }, + }, + { + id: '66', + data: { + cluster: 'B', + label: '66-B', + }, + }, + { + id: '67', + data: { + cluster: 'C', + label: '67-C', + }, + }, + { + id: '68', + data: { + cluster: 'B', + label: '68-B', + }, + }, + { + id: '69', + data: { + cluster: 'A', + label: '69-A', + }, + }, + { + id: '70', + data: { + cluster: 'C', + label: '70-C', + }, + }, + { + id: '71', + data: { + cluster: 'C', + label: '71-C', + }, + }, + { + id: '72', + data: { + cluster: 'B', + label: '72-B', + }, + }, + { + id: '73', + data: { + cluster: 'A', + label: '73-A', + }, + }, + { + id: '74', + data: { + cluster: 'A', + label: '74-A', + }, + }, + { + id: '75', + data: { + cluster: 'A', + label: '75-A', + }, + }, + { + id: '76', + data: { + cluster: 'C', + label: '76-C', + }, + }, + ], + edges: [ + { + id: 'edge-0.7919937071107459', + source: '1', + target: '0', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.12924522442330866', + source: '2', + target: '0', + data: { + cluster: 'c', + label: 'c', + value: 8, + }, + }, + { + id: 'edge-0.15868136439721114', + source: '3', + target: '0', + data: { + cluster: 'c', + label: 'c', + value: 10, + }, + }, + { + id: 'edge-0.30531183240520066', + source: '3', + target: '2', + data: { + cluster: 'c', + label: 'c', + value: 6, + }, + }, + { + id: 'edge-0.6993371051217985', + source: '4', + target: '0', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.32008810940555144', + source: '5', + target: '0', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.7036192821517431', + source: '6', + target: '0', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.9780633597165507', + source: '7', + target: '0', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.9991327158735921', + source: '8', + target: '0', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.6232076409214535', + source: '9', + target: '0', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.13383653855735256', + source: '11', + target: '10', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.4645927898350719', + source: '11', + target: '3', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.10895291682559893', + source: '11', + target: '2', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.24203387694071776', + source: '11', + target: '0', + data: { + cluster: 'c', + label: 'c', + value: 5, + }, + }, + { + id: 'edge-0.24340761842606917', + source: '12', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.6700692960070005', + source: '13', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.4993576901396357', + source: '14', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.4392008353958372', + source: '15', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.7186364793489599', + source: '17', + target: '16', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.9295381504956419', + source: '18', + target: '16', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.9321919328844319', + source: '18', + target: '17', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.8488064827860675', + source: '19', + target: '16', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.019225263510928903', + source: '19', + target: '17', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.14569270281368274', + source: '19', + target: '18', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.20156706228972188', + source: '20', + target: '16', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.5933623915542063', + source: '20', + target: '17', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.28402720987137453', + source: '20', + target: '18', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.9231363772457613', + source: '20', + target: '19', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.8400478902796711', + source: '21', + target: '16', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.7986332137023662', + source: '21', + target: '17', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.5099509479231421', + source: '21', + target: '18', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.17580037004969462', + source: '21', + target: '19', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.9947909494652307', + source: '21', + target: '20', + data: { + cluster: 'c', + label: 'c', + value: 5, + }, + }, + { + id: 'edge-0.911366490132252', + source: '22', + target: '16', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.7855474795149364', + source: '22', + target: '17', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.49890725311434925', + source: '22', + target: '18', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.9884549731704684', + source: '22', + target: '19', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.10898632990897617', + source: '22', + target: '20', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 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+ data: { + cluster: 'c', + label: 'c', + value: 9, + }, + }, + { + id: 'edge-0.16781632909628819', + source: '64', + target: '57', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.3684432329712777', + source: '64', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.9240086787315678', + source: '65', + target: '63', + data: { + cluster: 'c', + label: 'c', + value: 5, + }, + }, + { + id: 'edge-0.20292335803070793', + source: '65', + target: '64', + data: { + cluster: 'c', + label: 'c', + value: 7, + }, + }, + { + id: 'edge-0.6620084596799061', + source: '65', + target: '48', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.07844860042464741', + source: '65', + target: '62', + data: { + cluster: 'c', + label: 'c', + value: 5, + }, + }, + { + id: 'edge-0.4508322244613854', + source: '65', + target: '58', + data: { + cluster: 'c', + label: 'c', + value: 5, + }, + }, + { + id: 'edge-0.9696409251898224', + source: '65', + target: '61', + data: { + cluster: 'c', + label: 'c', + value: 5, + }, + }, + { + id: 'edge-0.9185671063399776', + source: '65', + target: '60', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.9305742965793917', + source: '65', + target: '59', + data: { + cluster: 'c', + label: 'c', + value: 5, + }, + }, + { + id: 'edge-0.7818091871332347', + source: '65', + target: '57', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.6871817754391591', + source: '65', + target: '55', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.05815153251154159', + source: '66', + target: '64', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.39750632866804936', + source: '66', + target: '58', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.49077392672658227', + source: '66', + target: '59', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.38623028748831323', + source: '66', + target: '62', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.4876908527910595', + source: '66', + target: '65', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.8023860247041521', + source: '66', + target: '48', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.04712757357754738', + source: '66', + target: '63', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.32938248904917544', + source: '66', + target: '61', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.532615877737582', + source: '66', + target: '60', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.25321931990310675', + source: '67', + target: '57', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.6970637417286427', + source: '68', + target: '25', + data: { + cluster: 'c', + label: 'c', + value: 5, + }, + }, + { + id: 'edge-0.1745885452575282', + source: '68', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.14206930744325996', + source: '68', + target: '24', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.9989150722241458', + source: '68', + target: '27', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.7682349827612385', + source: '68', + target: '48', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.7824172914834799', + source: '68', + target: '41', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.48456507218908107', + source: '69', + target: '25', + data: { + cluster: 'c', + label: 'c', + value: 6, + }, + }, + { + id: 'edge-0.082746223281976', + source: '69', + target: '68', + data: { + cluster: 'c', + label: 'c', + value: 6, + }, + }, + { + id: 'edge-0.1495975625850694', + source: '69', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.12286057140055262', + source: '69', + target: '24', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.62559397197263', + source: '69', + target: '27', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.6426532398064981', + source: '69', + target: '48', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.08895350359611132', + source: '69', + target: '41', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.6344276741888781', + source: '70', + target: '25', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.30489170301241253', + source: '70', + target: '69', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.8562671203701455', + source: '70', + target: '68', + data: { + cluster: 'b', + label: 'b', + value: 4, + }, + }, + { + id: 'edge-0.07758575999071282', + source: '70', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.47115334144676835', + source: '70', + target: '24', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.7586950852870196', + source: '70', + target: '27', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.05049809361203628', + source: '70', + target: '41', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.7897764467525927', + source: '70', + target: '58', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.047157143624002806', + source: '71', + target: '27', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.02534613025228083', + source: '71', + target: '69', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.952337500470199', + source: '71', + target: '68', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.6239954727046086', + source: '71', + target: '70', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.1447694643827877', + source: '71', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.8990149929089999', + source: '71', + target: '48', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.377352671552293', + source: '71', + target: '41', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.6862586927910777', + source: '71', + target: '25', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.4628836779364023', + source: '72', + target: '26', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.6706306275431415', + source: '72', + target: '27', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.6375672216055197', + source: '72', + target: '11', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.4552916866160963', + source: '73', + target: '48', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.9555376931749713', + source: '74', + target: '48', + data: { + cluster: 'b', + label: 'b', + value: 2, + }, + }, + { + id: 'edge-0.9397088813191374', + source: '74', + target: '73', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.06499589452378052', + source: '75', + target: '69', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.4830556640420829', + source: '75', + target: '68', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.8270959881107325', + source: '75', + target: '25', + data: { + cluster: 'b', + label: 'b', + value: 3, + }, + }, + { + id: 'edge-0.14592610569247477', + source: '75', + target: '48', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.7420487753432365', + source: '75', + target: '41', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.5921102833113367', + source: '75', + target: '70', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.37256890023646916', + source: '75', + target: '71', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.36405451119742116', + source: '76', + target: '64', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.21037855350254064', + source: '76', + target: '65', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.12772098572820068', + source: '76', + target: '66', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.8831572493064759', + source: '76', + target: '63', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.14777325440232292', + source: '76', + target: '62', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.38164885642116886', + source: '76', + target: '48', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + { + id: 'edge-0.3450217378352858', + source: '76', + target: '58', + data: { + cluster: 'a', + label: 'a', + value: 1, + }, + }, + ], +}; + +export const nodes202 = { + nodes: [ + { + id: '1', + data: { + cluster: 'A', + label: '1-A', + x: 301.20880899273163, + y: 16.498563471350245, + }, + }, + { + id: '2', + data: { + cluster: 'B', + label: '2-B', + x: 167.71579022730245, + y: 21.169092644111863, + }, + }, + { + id: '3', + data: { + cluster: 'C', + label: '3-C', + x: 225.60113755765104, + y: 53.38237357832761, + }, + }, + { + id: '4', + data: { + cluster: 'D', + label: '4-D', + x: 257.2171264302781, + y: 10.65803240773092, + }, + }, + { + id: '5', + data: { + cluster: 'E', + label: '5-E', + x: 631.9192989141275, + y: 16.185060075215628, + }, + }, + { + id: '6', + data: { + cluster: 'A', + label: '6-A', + x: 147.70380559678026, + y: 22.309088056991207, + }, + }, + { + id: '7', + data: { + cluster: 'B', + label: '7-B', + x: 127.84189789787449, + y: 93.67277180968523, + }, + }, + { + id: '8', + data: { + cluster: 'C', + label: '8-C', + x: 707.7325190040664, + y: 6.916731320922539, + }, + }, + { + id: '9', + data: { + cluster: 'D', + label: '9-D', + x: 235.0228391142811, + y: 4.721612635411866, + }, + }, + { + id: '10', + data: { + cluster: 'E', + label: '10-E', + x: 112.17360443437231, + y: 85.14322322856475, + }, + }, + { + id: '11', + data: { + cluster: 'A', + label: '11-A', + x: 151.94058879507244, + y: 39.805144202813125, + }, + }, + { + id: '12', + data: { + cluster: 'B', + label: '12-B', + x: 222.2986880590628, + y: 88.06947603346353, + }, + }, + { + id: '13', + data: { + cluster: 'C', + label: '13-C', + x: 604.6951590389173, + y: -53.95978614928713, + }, + }, + { + id: '14', + data: { + cluster: 'D', + label: '14-D', + x: 78.13869258561154, + y: 53.854113269521804, + }, + }, + { + id: '15', + data: { + cluster: 'E', + label: '15-E', + x: 57.32017518529654, + y: 104.76288631835605, + }, + }, + { + id: '16', + data: { + cluster: 'A', + label: '16-A', + x: 673.6741242664087, + y: 26.82550911323939, + }, + }, + { + id: '17', + data: { + cluster: 'B', + label: '17-B', + x: 155.47170625632668, + y: 116.73379987079107, + }, + }, + { + id: '18', + data: { + cluster: 'C', + label: '18-C', + x: 659.8004935950742, + y: -4.488657283902915, + }, + }, + { + id: '19', + data: { + cluster: 'D', + label: '19-D', + x: 599.0010245474674, + y: -20.64496545181059, + }, + }, + { + id: '20', + data: { + cluster: 'E', + label: '20-E', + x: 592.751970574288, + y: 75.641401815118, + }, + }, + { + id: '21', + data: { + cluster: 'A', + label: '21-A', + x: 430.06880990239205, + y: 31.849250143249904, + }, + }, + { + id: '22', + data: { + cluster: 'B', + label: '22-B', + x: 233.90199011505297, + y: 29.14332300354274, + }, + }, + { + id: '23', + data: { + cluster: 'C', + label: '23-C', + x: 140.53417335176366, + y: 104.24293269089377, + }, + }, + { + id: '24', + data: { + cluster: 'D', + label: '24-D', + x: 657.6323691943008, + y: -77.4386558440363, + }, + }, + { + id: '25', + data: { + cluster: 'E', + label: '25-E', + x: 104.62839595086363, + y: 34.708443038044265, + }, + }, + { + id: '26', + data: { + cluster: 'A', + label: '26-A', + x: 246.65151766730597, + y: 100.61314350776709, + }, + }, + { + id: '27', + data: { + cluster: 'B', + label: '27-B', + x: 287.73976326614627, + y: 93.6794945238267, + }, + }, + { + id: '28', + data: { + cluster: 'C', + label: '28-C', + x: 701.5483534740976, + y: -35.947008798702385, + }, + }, + { + id: '29', + data: { + cluster: 'D', + label: '29-D', + x: 18.817352550124035, + y: 81.91483226407264, + }, + }, + { + id: '30', + data: { + cluster: 'E', + label: '30-E', + x: 252.54666325089096, + y: -198.63655309420574, + }, + }, + { + id: '31', + data: { + cluster: 'A', + label: '31-A', + x: 733.8237869966578, + y: -90.26277289379352, + }, + }, + { + id: '32', + data: { + cluster: 'B', + label: '32-B', + x: 674.3876779304654, + y: -56.10785937643712, + }, + }, + { + id: '33', + data: { + cluster: 'C', + label: '33-C', + x: 199.6492065642083, + y: 1.7727263515040845, + }, + }, + { + id: '34', + data: { + cluster: 'D', + label: '34-D', + x: 574.8062458666107, + y: 23.966554376971402, + }, + }, + { + id: '35', + data: { + cluster: 'E', + label: '35-E', + x: 167.98611042812948, + y: 172.15142154155532, + }, + }, + { + id: '36', + data: { + cluster: 'A', + label: '36-A', + x: 1283.9532146261135, + y: 155.858407876654, + }, + }, + { + id: '37', + data: { + cluster: 'B', + label: '37-B', + x: 588.7131468576222, + y: 52.64924625939446, + }, + }, + { + id: '38', + data: { + cluster: 'C', + label: '38-C', + x: 691.8047077339269, + y: -132.2652032838732, + }, + }, + { + id: '39', + data: { + cluster: 'D', + label: 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+ id: 'edge-9560', + source: '26', + target: '97', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9580', + source: '85', + target: '79', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9600', + source: '85', + target: '89', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9620', + source: '85', + target: '157', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9640', + source: '167', + target: '125', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9660', + source: '90', + target: '153', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9680', + source: '90', + target: '89', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9700', + source: '90', + target: '98', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9720', + source: '133', + target: '103', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9740', + source: '133', + target: '64', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9760', + source: '133', + target: '168', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9780', + source: '133', + target: '155', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9800', + source: '34', + target: '46', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9820', + source: '35', + target: '80', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9840', + source: '35', + target: '89', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9860', + source: '35', + target: '143', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9880', + source: '125', + target: '21', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9900', + source: '126', + target: '5', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-9920', + source: '37', + target: '45', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9940', + source: '40', + target: '3', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9960', + source: '40', + target: '55', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-9980', + source: '40', + target: '95', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10000', + source: '97', + target: '14', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10020', + source: '97', + target: '59', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10040', + source: '97', + target: '60', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10060', + source: '41', + target: '77', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10080', + source: '41', + target: '166', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10100', + source: '173', + target: '73', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10120', + source: '173', + target: '55', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10140', + source: '173', + target: '61', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10160', + source: '102', + target: '101', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10180', + source: '157', + target: '15', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10200', + source: '157', + target: '86', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10220', + source: '157', + target: '99', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10240', + source: '2', + target: '73', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10260', + source: '2', + target: '83', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10280', + source: '2', + target: '92', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10300', + source: '51', + target: '12', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10320', + source: '51', + target: '166', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10340', + source: '103', + target: '106', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10360', + source: '73', + target: '6', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10380', + source: '73', + target: '58', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10400', + source: '7', + target: '27', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10420', + source: '7', + target: '58', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10440', + source: '7', + target: '136', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10460', + source: '9', + target: '11', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10480', + source: '9', + target: '21', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10500', + source: '9', + target: '95', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10520', + source: '178', + target: '28', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10540', + source: '165', + target: '22', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10560', + source: '11', + target: '3', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10580', + source: '11', + target: '142', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10600', + source: '12', + target: '4', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10620', + source: '12', + target: '193', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10640', + source: '12', + target: '94', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10660', + source: '13', + target: '32', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10680', + source: '14', + target: '55', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10700', + source: '76', + target: '27', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10720', + source: '76', + target: '87', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10740', + source: '130', + target: '49', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10760', + source: '77', + target: '54', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10780', + source: '77', + target: '142', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10800', + source: '77', + target: '99', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10820', + source: '45', + target: '20', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10840', + source: '86', + target: '79', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10860', + source: '86', + target: '88', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10880', + source: '86', + target: '99', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10900', + source: '164', + target: '82', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10920', + source: '164', + target: '136', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10940', + source: '166', + target: '79', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-10960', + source: '166', + target: '94', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-10980', + source: '89', + target: '10', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11000', + source: '89', + target: '87', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11020', + source: '158', + target: '20', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11040', + source: '155', + target: '153', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11060', + source: '155', + target: '61', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11080', + source: '60', + target: '17', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11100', + source: '60', + target: '91', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11120', + source: '61', + target: '153', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11140', + source: '61', + target: '94', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11160', + source: '91', + target: '55', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11180', + source: '143', + target: '27', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11200', + source: '143', + target: '142', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11220', + source: '99', + target: '3', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11240', + source: '99', + target: '93', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11260', + source: '95', + target: '193', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11280', + source: '136', + target: '80', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11300', + source: '136', + target: '92', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11320', + source: '4', + target: '21', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11340', + source: '6', + target: '27', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11360', + source: '6', + target: '172', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11380', + source: '52', + target: '67', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11400', + source: '74', + target: '78', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11420', + source: '74', + target: '94', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11440', + source: '10', + target: '172', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11460', + source: '92', + target: '59', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11480', + source: '92', + target: '134', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11500', + source: '152', + target: '30', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11520', + source: '17', + target: '79', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11540', + source: '78', + target: '153', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11560', + source: '79', + target: '27', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11580', + source: '79', + target: '184', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11600', + source: '141', + target: '88', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11620', + source: '59', + target: '21', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11640', + source: '87', + target: '192', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11660', + source: '87', + target: '80', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11680', + source: '27', + target: '88', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11700', + source: '134', + target: '55', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11720', + source: '93', + target: '142', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11740', + source: '94', + target: '84', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11760', + source: '39', + target: '48', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11780', + source: '98', + target: '172', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11800', + source: '153', + target: '168', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11820', + source: '80', + target: '58', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11840', + source: '193', + target: '58', + data: { + cluster: 'a', + label: 'a', + }, + }, + { + id: 'edge-11860', + source: '55', + target: '154', + data: { + cluster: 'c', + label: 'c', + }, + }, + { + id: 'edge-11880', + source: '172', + target: '22', + data: { + cluster: 'c', + label: 'c', + }, + }, + ], +}; + +export const nodes1589 = { + nodes: [ + { + "id": "0", + "data": { + "cluster": "A", + "label": "0-A", + "olabel": "Kuperman_M", + "x": 26.22602890442184, + "y": 33.48766498594824, + "degree": 1.4422495703074083 + } + }, + { + "id": "1", + "data": { + "cluster": "B", + "label": "1-B", + "olabel": "Acebron_J", + "x": 762.9747722969743, + "y": 1076.5655474780835, + "degree": 1.5874010519681994 + } + }, + { + "id": "2", + "data": { + "cluster": "C", + "label": "2-C", + "olabel": "Bonilla_L", + "x": 753.056692997006, + "y": 1017.9822431435797, + "degree": 1.5874010519681994 + } + }, + { + "id": "3", + "data": { + "cluster": "A", + "label": "3-A", + "olabel": "Perezvicente_C", + "x": 780.7209197522006, + "y": 1043.2465155564882, + "degree": 1.5874010519681994 + } + }, + { + "id": "4", + "data": { + "cluster": "B", + "label": "4-B", + "olabel": "Ritort_F", + "x": 720.7513914110218, + "y": 1031.8931814456985, + "degree": 1.5874010519681994 + } + }, + { + "id": "5", + "data": { + "cluster": "C", + "label": "5-C", + "olabel": "Spigler_R", + "x": 725.8939629672293, + "y": 1070.7323716825538, + "degree": 1.5874010519681994 + } + }, + { + "id": "6", + "data": { + "cluster": "A", + "label": "6-A", + "olabel": "Adamic_L", + "x": 232.80665735466172, + "y": 1024.7862385984115, + "degree": 1.5874010519681994 + } + }, + { + "id": "7", + "data": { + "cluster": "B", + "label": "7-B", + "olabel": "Adar_E", + "x": 282.21324055452686, + "y": 1040.805009893429, + "degree": 1 + } + }, + { + "id": "8", + "data": { + "cluster": "C", + "label": "8-C", + "olabel": "Huberman_B", + "x": 187.78093501739752, + "y": 984.9937052834931, + "degree": 1.8171205928321397 + } + }, + { + "id": "9", + "data": { + "cluster": "A", + "label": "9-A", + "olabel": "Lukose_R", + "x": 231.7726811466466, + "y": 980.0841215194478, + "degree": 1.4422495703074083 + } + }, + { + "id": "10", + "data": { + "cluster": "B", + "label": "10-B", + "olabel": "Puniyani_A", + "x": 186.81571738249275, + "y": 1027.3189294316453, + "degree": 1.4422495703074083 + } + }, + { + "id": "11", + "data": { + "cluster": "C", + "label": "11-C", + "olabel": "Aertsen_A", + "x": 169.631288622247, + "y": -318.82954507138595, + "degree": 1.912931182772389 + } + }, + { + "id": "12", + "data": { + "cluster": "A", + "label": "12-A", + "olabel": "Gerstein_G", + "x": 178.87731750009542, + "y": -377.02813683173326, + "degree": 1.4422495703074083 + } + }, + { + "id": "13", + "data": { + "cluster": "B", + "label": "13-B", + "olabel": "Habib_M", + "x": 207.16213271794734, + "y": 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"1576", + "data": { + "cluster": "A", + "label": "A", + "color": "#333" + } + }, + { + "id": "edge2735", + "source": "1578", + "target": "630", + "data": { + "cluster": "A", + "label": "A", + "color": "#333" + } + }, + { + "id": "edge2736", + "source": "1578", + "target": "629", + "data": { + "cluster": "C", + "label": "C", + "color": "#333" + } + }, + { + "id": "edge2737", + "source": "1580", + "target": "1579", + "data": { + "cluster": "A", + "label": "A", + "color": "#333" + } + }, + { + "id": "edge2738", + "source": "1583", + "target": "1582", + "data": { + "cluster": "A", + "label": "A", + "color": "#333" + } + }, + { + "id": "edge2739", + "source": "1585", + "target": "1584", + "data": { + "cluster": "B", + "label": "B", + "color": "#333" + } + }, + { + "id": "edge2740", + "source": "1586", + "target": "1584", + "data": { + "cluster": "C", + "label": "C", + "color": "#333" + } + }, + { + "id": "edge2741", + "source": "1587", + "target": "521", + "data": { + "cluster": "C", + "label": "C", + "color": "#333" + } + }, + { + "id": "edge2742", + "source": "1587", + "target": "75", + "data": { + "cluster": "A", + "label": "A", + "color": "#333" + } + } +], +}; diff --git a/__tests__/unit/gaddi.spec.ts b/__tests__/unit/gaddi.spec.ts new file mode 100644 index 0000000..143b420 --- /dev/null +++ b/__tests__/unit/gaddi.spec.ts @@ -0,0 +1,2440 @@ +import { Graph } from '@antv/graphlib'; +import { GADDI } from '../../packages/graph/src'; +import { nodes77, nodes202, nodes1589, nodes20 } from '../data/gaddi-test-data'; + +const data3 = { + nodes: [ + { + id: '0', + data: { + cluster: 'F', + }, + }, + { + id: '1', + data: { + cluster: 'F', + }, + }, + { + id: '2', + data: { + cluster: 'B', + }, + }, + { + id: '3', + data: { + cluster: 'B', + }, + }, + { + id: '4', + data: { + cluster: 'B', + }, + }, + { + id: '5', + data: { + cluster: 'B', + }, + }, + { + id: '6', + data: { + cluster: 'B', + }, + }, + { + id: '7', + data: { + cluster: 'B', + }, + }, + { + id: '8', + data: { + cluster: 'B', + }, + }, + { + id: '9', + data: { + cluster: 'B', + }, + }, + { + id: '10', + data: { + cluster: 'B', + }, + }, + { + id: '11', + data: { + cluster: 'B', + }, + }, + { + id: '12', + data: { + cluster: 'B', + }, + }, + { + id: '13', + data: { + cluster: 'B', + }, + }, + { + id: '14', + data: { + cluster: 'C', + }, + }, + { + id: '15', + data: { + cluster: 'C', + }, + }, + { + id: '16', + data: { + cluster: 'B', + }, + }, + { + id: '17', + data: { + cluster: 'C', + }, + }, + { + id: '18', + data: { + cluster: 'C', + }, + }, + { + id: '19', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.08338243451400973', + source: '0', + target: '1', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.9538984876112504', + source: '0', + target: '2', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.5610650101657038', + source: '1', + target: '3', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.6339807332841931', + source: '2', + target: '4', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.9110431395664749', + source: '2', + target: '5', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.17120532721066772', + source: '3', + target: '6', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.06410930423423311', + source: '3', + target: '7', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.7144629421427284', + source: '4', + target: '2', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.2697689400551728', + source: '4', + target: '9', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.5952356362426905', + source: '5', + target: '10', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.02101777124296822', + source: '6', + target: '11', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.966142346306492', + source: '6', + target: '12', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.5763065078005702', + source: '7', + target: '13', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.027081416081647047', + source: '8', + target: '14', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.620526080828502', + source: '8', + target: '15', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.8846666190325727', + source: '9', + target: '16', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.07190256509651016', + source: '10', + target: '16', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.1555601171040133', + source: '11', + target: '17', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.25060396902963533', + source: '11', + target: '18', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.8657381210149271', + source: '12', + target: '19', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.14987167523057177', + source: '13', + target: '19', + data: { + cluster: 'e', + }, + }, + ], +}; + +const pattern1 = { + nodes: [ + { + id: '0', + data: { + cluster: 'F', + }, + }, + { + id: '1', + data: { + cluster: 'F', + }, + }, + { + id: '2', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'pedge-1', + source: '0', + target: '1', + data: { + cluster: 'b', + }, + }, + { + id: 'pedge-2', + source: '0', + target: '2', + data: { + cluster: 'b', + }, + }, + ], +}; + +const pattern2 = { + nodes: [ + { + id: '11', + data: { + cluster: 'B', + }, + }, + { + id: '17', + data: { + cluster: 'C', + }, + }, + { + id: '18', + data: { + cluster: 'C', + }, + }, + ], + edges: [ + { + id: 'p2-edge-1', + source: '11', + target: '17', + data: { + cluster: 'c', + }, + }, + { + id: 'p2-edge-2', + source: '11', + target: '18', + data: { + cluster: 'b', + }, + }, + ], +}; + +const circlePattern = { + nodes: [ + { + id: '1', + data: { + cluster: 'B', + }, + }, + { + id: '2', + data: { + cluster: 'B', + }, + }, + { + id: '3', + data: { + cluster: 'B', + }, + }, + { + id: '4', + data: { + cluster: 'B', + }, + }, + { + id: '5', + data: { + cluster: 'B', + }, + }, + { + id: '6', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.6743779589170014', + source: '1', + target: '2', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.22861083620146805', + source: '2', + target: '3', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.28467629792241245', + source: '3', + target: '4', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.9617896696169548', + source: '4', + target: '5', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.4618290406313039', + source: '5', + target: '6', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.10413260508650302', + source: '6', + target: '1', + data: { + cluster: 'e', + }, + }, + ], +}; + +describe('gSpan', () => { + it('gSpan match pattern 1', () => { + const graphCore = new Graph(data3); + const matchedSubGraphs = GADDI( + graphCore, + pattern1, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + expect(matchedSubGraphs.length).toBe(2); + matchedSubGraphs.forEach((graph) => { + expect(graph.nodes[0].data.cluster).toBe('B'); + expect(graph.nodes[1].data.cluster).toBe('F'); + expect(graph.nodes[2].data.cluster).toBe('F'); + expect(graph.edges[0].data.cluster).toBe('b'); + expect(graph.edges[1].data.cluster).toBe('b'); + }); + }); + it('gSpan match pattern 2', () => { + const matchedSubGraphs = GADDI( + new Graph(data3), + pattern2, + false, + 2, + 1, + 'cluster', + 'cluster' + ); + console.log('test2', matchedSubGraphs); + expect(matchedSubGraphs.length).toBe(1); + // expect(matchedSubGraphs.nodes) + }); + it('gSpan match circular', () => { + const matchedSubGraphs = GADDI( + new Graph(data3), + circlePattern, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + expect(matchedSubGraphs.length).toBe(2); + matchedSubGraphs.forEach((graph) => { + graph.nodes.forEach((node) => { + expect(node.data.cluster).toBe('B'); + }); + graph.edges.forEach((edge) => { + expect(edge.data.cluster).toBe('e'); + }); + }); + }); + it('gSpan match circular2 with a parallel edge', () => { + const circlePattern2 = { + nodes: [ + { + id: '1', + data: { + cluster: 'B', + }, + }, + { + id: '2', + data: { + cluster: 'B', + }, + }, + { + id: '3', + data: { + cluster: 'B', + }, + }, + { + id: '4', + data: { + cluster: 'B', + }, + }, + { + id: '5', + data: { + cluster: 'B', + }, + }, + { + id: '6', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.42477369317166525', + source: '1', + target: '2', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.8428047456975589', + source: '2', + target: '3', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.9656786081779836', + source: '3', + target: '4', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.5334304163594565', + source: '4', + target: '5', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.692950106628371', + source: '4', + target: '5', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.7740666865191763', + source: '5', + target: '6', + data: { + cluster: 'e', + }, + }, + { + id: 'edge-0.6494155903568832', + source: '6', + target: '1', + data: { + cluster: 'e', + }, + }, + ], + }; + const matchedSubGraphs = GADDI( + new Graph(data3), + circlePattern2, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log('circle 2', matchedSubGraphs); + expect(matchedSubGraphs.length).toBe(1); + + matchedSubGraphs[0].nodes.forEach((node) => { + expect(node.data.cluster).toBe('B'); + }); + matchedSubGraphs[0].edges.forEach((edge, i) => { + if (i === 2) { + expect(edge.data.cluster).toBe('b'); + return; + } + expect(edge.data.cluster).toBe('e'); + }); + }); + // 平行边可能被匹配成多条单独边 + it('gSpan match two parallel edges', () => { + const pattern3 = { + nodes: [ + { id: 'node1', data: { cluster: 'B' } }, + { id: 'node2', data: { cluster: 'B' } }, + ], + edges: [ + { + id: 'p3-edge-1', + source: 'node1', + target: 'node2', + data: { cluster: 'b' }, + }, + { + id: 'p3-edge-2', + source: 'node1', + target: 'node2', + data: { cluster: 'e' }, + }, + ], + }; + const matchedSubGraphs = GADDI( + new Graph(data3), + pattern3, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log('test3', matchedSubGraphs); + // expect(matchedSubGraphs.length).toBe(2); + // matchedSubGraphs.forEach((graph) => { + // expect(graph.nodes[0].data.cluster).toBe("F"); + // expect(graph.nodes[1].data.cluster).toBe("F"); + // expect(graph.nodes[2].data.cluster).toBe("B"); + // expect(graph.edges[0].data.cluster).toBe("b"); + // expect(graph.edges[1].data.cluster).toBe("b"); + // }); + }); +}); + +describe('gSpan directed', () => { + it('gSpan match pattern 1', () => { + const pattern11 = { + nodes: [ + { + id: '0', + data: { + cluster: 'F', + }, + }, + { + id: '1', + data: { + cluster: 'F', + }, + }, + { + id: '2', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.08192985717375234', + source: '0', + target: '1', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.3680626849736408', + source: '0', + target: '2', + data: { + cluster: 'b', + }, + }, + ], + }; + const matchedSubGraphs = GADDI( + new Graph(data3), + pattern11, + true, + undefined, + undefined, + 'cluster', + 'cluster' + ); + expect(matchedSubGraphs.length).toBe(2); + expect(matchedSubGraphs[0].nodes[0].id).toBe('2'); + expect(matchedSubGraphs[0].nodes[1].id).toBe('0'); + expect(matchedSubGraphs[0].nodes[2].id).toBe('1'); + }); +}); + +describe('GADDI switch nodes', () => { + it('gSpan match pattern 1', () => { + const pattern1 = { + nodes: [ + { id: 'Person', data: { dataType: 'Person' } }, + { id: 'Enterprise', data: { dataType: 'Enterprise' } }, + ], + edges: [ + { + id: 'edge-1613700998017', + source: 'Person', + target: 'Enterprise', + data: { + dataType: 'Person2Enterprise#Guarantee', + rules: [], + }, + }, + ], + }; + const res1 = GADDI( + new Graph(nodes20), + pattern1, + true, + undefined, + undefined, + 'dataType', + 'dataType' + ); + expect(res1.length).toBe(6); + const pattern2 = { + nodes: [ + { id: 'Enterprise', data: { dataType: 'Enterprise' } }, + { id: 'Person', data: { dataType: 'Person' } }, + ], + edges: [ + { + id: 'edge-1613700998017', + source: 'Person', + target: 'Enterprise', + data: { + dataType: 'Person2Enterprise#Guarantee', + rules: [], + }, + }, + ], + }; + const res2 = GADDI( + new Graph(nodes20), + pattern2, + true, + undefined, + undefined, + 'dataType', + 'dataType' + ); + expect(res2.length).toBe(6); + }); +}); + +describe('Performance: gSpan 77 nodes G', () => { + // 100ms + it('pattern 3 nodes', () => { + const patternWith3Nodes = { + nodes: [ + { id: 'pn1', data: { cluster: 'A' } }, + { id: 'pn2', data: { cluster: 'B' } }, + { id: 'pn3', data: { cluster: 'A' } }, + ], + edges: [ + { + id: 'p4-edge-1', + source: 'pn1', + target: 'pn2', + data: { cluster: 'b' }, + }, + { + id: 'p4-edge-2', + source: 'pn1', + target: 'pn3', + data: { cluster: 'a' }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes77), + patternWith3Nodes, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log( + '77 nodes graph matching 3 nodes pattern', + performance.now() - begin + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + + expect(result.length).toBe(5); + expect(result[0].nodes[0].id).toBe('11'); + expect(result[1].nodes[0].id).toBe('48'); + expect(result[2].nodes[0].id).toBe('60'); + expect(result[3].nodes[0].id).toBe('63'); + expect(result[4].nodes[0].id).toBe('66'); + + expect(result[3].nodes.length).toBe(4); + expect(result[4].nodes.length).toBe(3); + }); + // 100ms + it('pattern 5 nodes', () => { + const patternWith5Nodes = { + nodes: [ + { + id: 'pn1', + data: { + cluster: 'A', + }, + }, + { + id: 'pn2', + data: { + cluster: 'C', + }, + }, + { + id: 'pn3', + data: { + cluster: 'C', + }, + }, + { + id: 'pn4', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.7443251984519861', + source: 'pn1', + target: 'pn2', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.9853001965849568', + source: 'pn1', + target: 'pn3', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.4020452668717929', + source: 'pn3', + target: 'pn2', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.17850753263738572', + source: 'pn3', + target: 'pn4', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.11349992003063503', + source: 'pn2', + target: 'pn4', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.9896596436859824', + source: 'pn1', + target: 'pn4', + data: { + cluster: 'c', + }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes77), + patternWith5Nodes, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log( + '77 nodes graph matching 5 nodes pattern', + performance.now() - begin + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + expect(result.length).toBe(2); + expect(result[0].nodes[0].id).toBe('11'); + expect(result[1].nodes[0].id).toBe('26'); + + expect(result[0].nodes.length).toBe(7); + expect(result[1].nodes.length).toBe(4); + }); + it('pattern 10 nodes', () => { + const patternWith10Nodes = { + nodes: [ + { + id: 'pn1', + data: { + cluster: 'B', + }, + }, + { + id: 'pn2', + data: { + cluster: 'B', + }, + }, + { + id: 'pn3', + data: { + cluster: 'B', + }, + }, + { + id: 'pn4', + data: { + cluster: 'C', + }, + }, + { + id: 'pn5', + data: { + cluster: 'B', + }, + }, + { + id: 'pn6', + data: { + cluster: 'A', + }, + }, + { + id: 'pn7', + data: { + cluster: 'B', + }, + }, + { + id: 'pn8', + data: { + cluster: 'B', + }, + }, + { + id: 'pn9', + data: { + cluster: 'A', + }, + }, + { + id: 'pn10', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.49811811736590594', + source: 'pn1', + target: 'pn2', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.3108286621514387', + source: 'pn1', + target: 'pn3', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.5362696318738058', + source: 'pn1', + target: 'pn4', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.2581433676228688', + source: 'pn1', + target: 'pn5', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.8075488833250957', + source: 'pn1', + target: 'pn6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.21127614133068606', + source: 'pn1', + target: 'pn7', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.1563969630013453', + source: 'pn1', + target: 'pn8', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.6010043061195691', + source: 'pn2', + target: 'pn3', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.13859898720996888', + source: 'pn2', + target: 'pn4', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.11287184351593438', + source: 'pn2', + target: 'pn5', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.853138911525565', + source: 'pn2', + target: 'pn6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.9545662639947321', + source: 'pn2', + target: 'pn7', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.16149044382902744', + source: 'pn2', + target: 'pn8', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.5844470600554277', + source: 'pn3', + target: 'pn4', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.27134167856974867', + source: 'pn3', + target: 'pn5', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.2851054184506281', + source: 'pn3', + target: 'pn6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.7040742308385957', + source: 'pn3', + target: 'pn7', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.8893916753255282', + source: 'pn3', + target: 'pn8', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.8506246275860265', + source: 'pn4', + target: 'pn5', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.158284553750363', + source: 'pn4', + target: 'pn6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.5973775878848873', + source: 'pn4', + target: 'pn7', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.194214404474649', + source: 'pn4', + target: 'pn8', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.11496528435188291', + source: 'pn5', + target: 'pn6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.9403238130895326', + source: 'pn5', + target: 'pn7', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.1555665480046966', + source: 'pn5', + target: 'pn8', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.3176240353598032', + source: 'pn6', + target: 'pn7', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.682857971789463', + source: 'pn6', + target: 'pn8', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.7065126054775828', + source: 'pn7', + target: 'pn8', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.571132365680477', + source: 'pn8', + target: 'pn9', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.3772610754231518', + source: 'pn8', + target: 'pn10', + data: { + cluster: 'a', + }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes77), + patternWith10Nodes, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log( + '77 nodes graph matching 10 nodes pattern', + performance.now() - begin, + result.length + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + expect(result.length).toBe(3); + + expect(result[0].nodes[0].id).toBe('16'); + expect(result[1].nodes[0].id).toBe('17'); + expect(result[2].nodes[0].id).toBe('23'); + }); +}); + +describe('Performance: 202 nodes G', () => { + it('pattern with 4 nodes', () => { + const patternWith4Nodes = { + nodes: [ + { + id: 'pn1', + data: { + cluster: 'E', + }, + }, + { + id: 'pn2', + data: { + cluster: 'D', + }, + }, + { + id: 'pn3', + data: { + cluster: 'B', + }, + }, + { + id: 'pn4', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.3462664680646945', + source: 'pn1', + target: 'pn2', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.3659087892682116', + source: 'pn2', + target: 'pn3', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.4061347706895744', + source: 'pn3', + target: 'pn4', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.26416200117867406', + source: 'pn1', + target: 'pn4', + data: { + cluster: 'c', + }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes202), + patternWith4Nodes, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log( + '202 nodes graph matching 4 nodes pattern', + performance.now() - begin + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + expect(result.length).toBe(1); + expect(result[0].nodes[0].id).toBe('67'); + expect(result[0].nodes.length).toBe(4); + }); + it('pattern with 7 nodes', () => { + const patternWith7Nodes = { + nodes: [ + { + id: 'pn1', + data: { + cluster: 'B', + }, + }, + { + id: 'pn2', + data: { + cluster: 'C', + }, + }, + { + id: 'pn3', + data: { + cluster: 'C', + }, + }, + { + id: 'pn4', + data: { + cluster: 'D', + }, + }, + { + id: 'pn5', + data: { + cluster: 'E', + }, + }, + { + id: 'pn6', + data: { + cluster: 'C', + }, + }, + { + id: 'pn7', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.5870526666138531', + source: 'pn1', + target: 'pn2', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.6788288914519547', + source: 'pn2', + target: 'pn3', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.8064334345924178', + source: 'pn3', + target: 'pn4', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.9374896941202728', + source: 'pn4', + target: 'pn5', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.9184405554911137', + source: 'pn5', + target: 'pn1', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.07761229167201766', + source: 'pn4', + target: 'pn6', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.21237311448001783', + source: 'pn4', + target: 'pn7', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.2894099278321791', + source: 'pn6', + target: 'pn7', + data: { + cluster: 'c', + }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes202), + patternWith7Nodes, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log( + '202 nodes graph matching 7 nodes pattern', + performance.now() - begin + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + expect(result.length).toBe(6); + expect(result[0].nodes[0].id).toBe('2'); + expect(result[1].nodes[0].id).toBe('77'); + expect(result[2].nodes[0].id).toBe('87'); + expect(result[3].nodes[0].id).toBe('132'); + expect(result[4].nodes[0].id).toBe('157'); + expect(result[5].nodes[0].id).toBe('167'); + }); + it('pattern with 7 nodes, directed', () => { + const patternWith7Nodes = { + nodes: [ + { + id: 'pn1', + data: { + cluster: 'B', + }, + }, + { + id: 'pn2', + data: { + cluster: 'C', + }, + }, + { + id: 'pn3', + data: { + cluster: 'C', + }, + }, + { + id: 'pn4', + data: { + cluster: 'D', + }, + }, + { + id: 'pn5', + data: { + cluster: 'E', + }, + }, + { + id: 'pn6', + data: { + cluster: 'C', + }, + }, + { + id: 'pn7', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.898607921909873', + source: 'pn1', + target: 'pn2', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.6226469518013837', + source: 'pn1', + target: 'pn5', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.6066686967978825', + source: 'pn3', + target: 'pn2', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.07826364791706686', + source: 'pn4', + target: 'pn3', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.28260824415873587', + source: 'pn5', + target: 'pn4', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.1189109394923169', + source: 'pn4', + target: 'pn6', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.23919265487780295', + source: 'pn4', + target: 'pn7', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.5510249949377384', + source: 'pn7', + target: 'pn6', + data: { + cluster: 'c', + }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes202), + patternWith7Nodes, + true, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log( + '202 nodes graph matching 7 nodes pattern, directed', + performance.now() - begin + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + expect(result.length).toBe(1); + expect(result[0].nodes[0].id).toBe('167'); + expect(result[0].nodes.length).toBe(7); + }); + it('pattern with 14 nodes, directed', () => { + const patternWith14Nodes = { + nodes: [ + { + id: 'pn1', + data: { + cluster: 'D', + }, + }, + { + id: 'pn2', + data: { + cluster: 'C', + }, + }, + { + id: 'pn3', + data: { + cluster: 'D', + }, + }, + { + id: 'pn4', + data: { + cluster: 'D', + }, + }, + { + id: 'pn5', + data: { + cluster: 'C', + }, + }, + { + id: 'pn6', + data: { + cluster: 'B', + }, + }, + { + id: 'pn7', + data: { + cluster: 'E', + }, + }, + { + id: 'pn8', + data: { + cluster: 'C', + }, + }, + { + id: 'pn9', + data: { + cluster: 'C', + }, + }, + { + id: 'pn10', + data: { + cluster: 'B', + }, + }, + { + id: 'pn11', + data: { + cluster: 'B', + }, + }, + { + id: 'pn12', + data: { + cluster: 'A', + }, + }, + { + id: 'pn13', + data: { + cluster: 'E', + }, + }, + { + id: 'pn14', + data: { + cluster: 'B', + }, + }, + ], + edges: [ + { + id: 'edge-0.7106061009077658', + source: 'pn2', + target: 'pn1', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.628968548905904', + source: 'pn3', + target: 'pn1', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.8416466431671159', + source: 'pn4', + target: 'pn1', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.7302767990647832', + source: 'pn1', + target: 'pn5', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.5467945218547585', + source: 'pn6', + target: 'pn1', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.15267563895766645', + source: 'pn7', + target: 'pn1', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.26286279393723255', + source: 'pn8', + target: 'pn1', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.9159459623063093', + source: 'pn1', + target: 'pn9', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.272940049859387', + source: 'pn10', + target: 'pn1', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.2950771917502619', + source: 'pn11', + target: 'pn1', + data: { + cluster: 'a', + }, + }, + { + id: 'edge-0.3768706834813713', + source: 'pn1', + target: 'pn12', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.9004083636419395', + source: 'pn1', + target: 'pn13', + data: { + cluster: 'c', + }, + }, + { + id: 'edge-0.9613119889463126', + source: 'pn14', + target: 'pn13', + data: { + cluster: 'c', + }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes202), + patternWith14Nodes, + true, + undefined, + undefined, + 'cluster', + 'cluster' + ); + + console.log( + '202 nodes graph matching 14 nodes pattern, directed', + performance.now() - begin, + result.length + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + // expect(result.length).toBe(8); + // expect(result[7].nodes[0].id).toBe("100"); + }); +}); +describe('Performance: 1589 nodes G', () => { + it('pattern with 4 nodes', () => { + const patternWith4Nodes = { + nodes: [ + { + id: 'pn1', + data: { + cluster: 'A', + }, + }, + { + id: 'pn2', + data: { + cluster: 'B', + }, + }, + { + id: 'pn3', + data: { + cluster: 'C', + }, + }, + { + id: 'pn4', + data: { + cluster: 'A', + }, + }, + ], + edges: [ + { + id: 'edge-0.7019124790843014', + source: 'pn1', + target: 'pn2', + data: { + cluster: 'B', + }, + }, + { + id: 'edge-0.7694967395320613', + source: 'pn1', + target: 'pn3', + data: { + cluster: 'C', + }, + }, + { + id: 'edge-0.17986599787898694', + source: 'pn2', + target: 'pn3', + data: { + cluster: 'A', + }, + }, + { + id: 'edge-0.17450508771099238', + source: 'pn3', + target: 'pn4', + data: { + cluster: 'C', + }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes1589), + patternWith4Nodes, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log( + '1589 nodes graph matching 4 nodes pattern', + performance.now() - begin, + result.length + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + + expect(result.length).toBe(163); + }); + // TODO: 爆栈 + it('pattern with 6 nodes full-connected', () => { + const patternWith6Nodes = { + nodes: [ + { + id: 'pn1', + data: { + cluster: 'B', + }, + }, + { + id: 'pn2', + data: { + cluster: 'C', + }, + }, + { + id: 'pn3', + data: { + cluster: 'A', + }, + }, + { + id: 'pn4', + data: { + cluster: 'C', + }, + }, + { + id: 'pn5', + data: { + cluster: 'B', + }, + }, + { + id: 'pn6', + data: { + cluster: 'A', + }, + }, + ], + edges: [ + { + id: 'edge-0.13788832813458018', + source: 'pn1', + target: 'pn2', + data: { + cluster: 'A', + }, + }, + { + id: 'edge-0.23939194653978513', + source: 'pn1', + target: 'pn3', + data: { + cluster: 'B', + }, + }, + { + id: 'edge-0.41745846944637144', + source: 'pn1', + target: 'pn4', + data: { + cluster: 'A', + }, + }, + { + id: 'edge-0.4509154730355722', + source: 'pn1', + target: 'pn5', + data: { + cluster: 'C', + }, + }, + { + id: 'edge-0.9257354686511656', + source: 'pn1', + target: 'pn6', + data: { + cluster: 'B', + }, + }, + { + id: 'edge-0.21124048973496912', + source: 'pn2', + target: 'pn3', + data: { + cluster: 'C', + }, + }, + { + id: 'edge-0.7475789066195164', + source: 'pn2', + target: 'pn4', + data: { + cluster: 'B', + }, + }, + { + id: 'edge-0.57667006311099', + source: 'pn2', + target: 'pn5', + data: { + cluster: 'A', + }, + }, + { + id: 'edge-0.37221806660871226', + source: 'pn2', + target: 'pn6', + data: { + cluster: 'C', + }, + }, + { + id: 'edge-0.9492883979946696', + source: 'pn3', + target: 'pn3', + data: { + cluster: 'C', + }, + }, + { + id: 'edge-0.9568204100929036', + source: 'pn3', + target: 'pn5', + data: { + cluster: 'B', + }, + }, + { + id: 'edge-0.056762818943291604', + source: 'pn3', + target: 'pn6', + data: { + cluster: 'A', + }, + }, + { + id: 'edge-0.12518022695734143', + source: 'pn4', + target: 'pn5', + data: { + cluster: 'A', + }, + }, + { + id: 'edge-0.7730404042634065', + source: 'pn4', + target: 'pn6', + data: { + cluster: 'C', + }, + }, + { + id: 'edge-0.17324349736721834', + source: 'pn5', + target: 'pn6', + data: { + cluster: 'B', + }, + }, + ], + }; + const begin = performance.now(); + const result = GADDI( + new Graph(nodes1589), + patternWith6Nodes, + false, + undefined, + undefined, + 'cluster', + 'cluster' + ); + console.log( + '1589 nodes graph matching 6 nodes full-connected pattern', + performance.now() - begin, + result.length + ); + result.forEach((re) => { + console.log(JSON.stringify(re)); + }); + }); +}); + +describe('Prune', () => { + it('Prune', () => { + const dataPrune = { + nodes: [ + { + id: '0', + data: { + cluster: 'B', + }, + }, + { + id: '1', + data: { + cluster: 'B', + }, + }, + { + id: '2', + data: { + cluster: 'B', + }, + }, + { + id: '3', + data: { + cluster: 'C', + }, + }, + ], + edges: [ + { + id: 'edge-0.29440279439195516', + source: '0', + target: '1', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.7825543504752381', + source: '1', + target: '2', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.6413307372249752', + source: '1', + target: '3', + data: { + cluster: 'b', + }, + }, + ], + }; + + const dataPrune2 = { + nodes: [ + { + id: '0', + data: { + cluster: 'B', + }, + }, + { + id: '1', + data: { + cluster: 'B', + }, + }, + { + id: '2', + data: { + cluster: 'B', + }, + }, + { + id: '3', + data: { + cluster: 'B', + }, + }, + { + id: '4', + data: { + cluster: 'B', + }, + }, + { + id: '5', + data: { + cluster: 'B', + }, + }, + { + id: '6', + data: { + cluster: 'B', + }, + }, + { + id: '7', + data: { + cluster: 'B', + }, + }, + { + id: '8', + data: { + cluster: 'B', + }, + }, + { + id: '9', + data: { + cluster: 'B', + }, + }, + { + id: '10', + data: { + cluster: 'B', + }, + }, + { + id: '11', + data: { + cluster: 'C', + }, + }, + ], + edges: [ + { + id: 'edge-0.6062332351158723', + source: '0', + target: '6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.1530238502615775', + source: '1', + target: '6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.06311710747585053', + source: '2', + target: '6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.7767523461330159', + source: '3', + target: '6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.410684436479148', + source: '4', + target: '6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.43896900099152525', + source: '5', + target: '6', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.8940589416958993', + source: '6', + target: '7', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.17819639720121416', + source: '6', + target: '8', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.4586136024892884', + source: '6', + target: '9', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.15938072661257174', + source: '6', + target: '10', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.19298427403165586', + source: '6', + target: '11', + data: { + cluster: 'b', + }, + }, + ], + }; + const p = { + nodes: [ + { + id: '0', + data: { + cluster: 'B', + }, + }, + { + id: '1', + data: { + cluster: 'B', + }, + }, + { + id: '2', + data: { + cluster: 'B', + }, + }, + { + id: '3', + data: { + cluster: 'C', + }, + }, + ], + edges: [ + { + id: 'edge-0.3714552255077763', + source: '0', + target: '1', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.28421618356248635', + source: '2', + target: '1', + data: { + cluster: 'b', + }, + }, + { + id: 'edge-0.47947209208258723', + source: '1', + target: '3', + data: { + cluster: 'b', + }, + }, + ], + }; + + const result = GADDI( + new Graph(dataPrune2), + p, + true, + undefined, + undefined, + 'cluster', + 'cluster' + ); + + // console.log('res', result); + // // console.log(JSON.stringify(data3)); + + // result.forEach(re => { + // console.log(JSON.stringify(re)); + // }); + expect(result.length).toBe(1); + }); +}); diff --git a/packages/graph/src/gSpan/gSpan.ts b/packages/graph/src/gSpan/gSpan.ts new file mode 100644 index 0000000..c9a02b0 --- /dev/null +++ b/packages/graph/src/gSpan/gSpan.ts @@ -0,0 +1,982 @@ +import { clone, uniqueId } from '@antv/util'; +import { GraphData } from '../types'; +import { + Graph, + Edge, + VACANT_NODE_LABEL, + VACANT_GRAPH_ID, + Node, + VACANT_EDGE_LABEL, +} from './struct'; + +export interface EdgeMap { + [key: string]: { + // key formatted as source-target + idx: number; // The index of the edge in the original graphData.edges. + edge: any; + }; +} + +export interface NodeMap { + [key: string]: { + // key formatted as node.id + idx: number; // The index of the node in the original graphData.nodes. + node: any; + degree: number; + inDegree: number; + outDegree: number; + }; +} + +interface PDFS { + graphId: number; + edge: any; + preNode: any; +} + +class DFSedge { + public fromNode: number; + public toNode: number; + public nodeEdgeNodeLabel: { + nodeLabel1: string; + edgeLabel: string; + nodeLabel2: string; + }; + + constructor( + fromNode: number, + toNode: number, + fromNodeLabel: string, + edgeLabel: string, + toNodeLabel: string + ) { + this.fromNode = fromNode; + this.toNode = toNode; + this.nodeEdgeNodeLabel = { + nodeLabel1: fromNodeLabel || VACANT_NODE_LABEL, + edgeLabel: edgeLabel || VACANT_EDGE_LABEL, + nodeLabel2: toNodeLabel || VACANT_NODE_LABEL, + }; + } + + equalTo(other: DFSedge) { + return ( + this.fromNode === other.fromNode && + this.toNode === other.toNode && + this.nodeEdgeNodeLabel === other.nodeEdgeNodeLabel + ); + } + + notEqualTo(other: DFSedge) { + return !this.equalTo(other); + } +} + +// DFScode is the array of DESedge +class DFScode { + public dfsEdgeList: DFSedge[]; + public rmpath: any; + + constructor() { + this.rmpath = []; + this.dfsEdgeList = []; + } + + equalTo(other: DFSedge[]) { + const aLength = this.dfsEdgeList.length; + const bLength = other.length; + if (aLength !== bLength) return false; + for (let i = 0; i < aLength; i++) { + if (this.dfsEdgeList[i] !== other[i]) return false; + } + return true; + } + + notEqualTo(other: DFSedge[]) { + return !this.equalTo(other); + } + + /** Add an edge to DFScode */ + pushBack( + fromNode: number, + toNode: number, + fromNodeLabel: string, + edgeLabel: string, + toNodeLabel: string + ) { + this.dfsEdgeList.push( + new DFSedge(fromNode, toNode, fromNodeLabel, edgeLabel, toNodeLabel) + ); + return this.dfsEdgeList; + } + + /** Build graph according to dfs */ + toGraph(graphId: number = VACANT_GRAPH_ID, directed = false) { + const graph = new Graph(graphId, true, directed); + this.dfsEdgeList.forEach((dfsEdge) => { + const fromNodeId = dfsEdge.fromNode; + const toNodeId = dfsEdge.toNode; + const { nodeLabel1, edgeLabel, nodeLabel2 } = dfsEdge.nodeEdgeNodeLabel; + + if (nodeLabel1 !== VACANT_NODE_LABEL) { + graph.addNode(fromNodeId, nodeLabel1); + } + if (nodeLabel2 !== VACANT_NODE_LABEL) graph.addNode(toNodeId, nodeLabel2); + if (nodeLabel1 !== VACANT_NODE_LABEL && nodeLabel2 !== nodeLabel1) { + graph.addEdge(undefined, fromNodeId, toNodeId, edgeLabel); + } + }); + return graph; + } + + // Build rightmost path + buildRmpath() { + this.rmpath = []; + let oldFrom = undefined; + const selfLength = this.dfsEdgeList.length; + for (let i = selfLength - 1; i >= 0; i--) { + const dfsEdge = this.dfsEdgeList[i]; + const fromNodeIdx = dfsEdge.fromNode; + const toNodeIdx = dfsEdge.toNode; + if ( + fromNodeIdx < toNodeIdx && + (oldFrom === undefined || toNodeIdx === oldFrom) + ) { + this.rmpath.push(i); + oldFrom = fromNodeIdx; + } + } + return this.rmpath; + } + + getNodeNum() { + const nodeMap: { [key: number]: boolean } = {}; + this.dfsEdgeList.forEach((dfsEdge) => { + if (!nodeMap[dfsEdge.fromNode]) nodeMap[dfsEdge.fromNode] = true; + if (!nodeMap[dfsEdge.toNode]) nodeMap[dfsEdge.toNode] = true; + }); + return Object.keys(nodeMap).length; + } +} + +class History { + public his: object; + public edges: Edge[]; + public nodesUsed: { [key: number]: number }; + public edgesUsed: { [key: number]: number }; + + constructor(pdfs: PDFS) { + this.his = {}; + this.nodesUsed = {}; + this.edgesUsed = {}; + this.edges = []; + if (!pdfs) return; + let pdfsIterator = pdfs; + while (pdfsIterator) { + const e = pdfsIterator.edge; + this.edges.push(e); + this.nodesUsed[e.from] = 1; + this.nodesUsed[e.to] = 1; + this.edgesUsed[e.id] = 1; + pdfsIterator = pdfsIterator.preNode; + } + // reverse the order + this.edges = this.edges.reverse(); + } + + hasNode(node: Node) { + return this.nodesUsed[node.id] === 1; + } + + hasEdge(edge: Edge) { + return this.edgesUsed[edge.id] === 1; + } +} + +interface Root { + [key: string]: { + projected: PDFS[]; + nodeLabel1?: string; + edgeLabel?: string; + nodeLabel2?: string; + fromNodeId?: number; + toNodeId?: number; + }; +} + +interface GraphDataMap { + [key: string]: GraphData; +} +interface GraphMap { + [key: number]: Graph; +} + +interface AlgorithmProps { + graphs: GraphMap; // Graph data + minSupport: number; // Minimum support count, specified based on the number of graphs in the graphs list + directed?: boolean; // Whether it is a directed graph, false by default + minNodeNum?: number; // The minimum number of edges in each subgraph. Default is 1 + maxNodeNum?: number; // The maximum number of edges in each subgraph. Default is 4 + top?: number; // Return the top frequent subgraphs. Default is 10 + verbose?: boolean; +} + +class GSpan { + public graphs: GraphMap; + public dfsCode: DFScode; + public support: number; + public frequentSize1Subgraphs: GraphData[]; + public frequentSubgraphs: Graph[]; + public reportDF: []; + public maxNodeNum: number; + public minNodeNum: number; + public minSupport: number; + public top: number; + public directed: boolean; + private counter: number; // The ID used for generating the graph, incremented automatically. + public verbose: boolean; + + constructor({ + graphs, + minSupport = 2, + minNodeNum = 1, + maxNodeNum = 4, + top = 10, + directed = false, + verbose = false, + }: AlgorithmProps) { + // -------- Step 0: Initailizing ------- + this.graphs = graphs; + this.dfsCode = new DFScode(); + this.support = 0; + this.frequentSize1Subgraphs = []; + this.frequentSubgraphs = []; + this.minSupport = minSupport; + this.top = top; + this.directed = directed; + this.counter = 0; + // TODO? timestamp = {} + this.maxNodeNum = maxNodeNum; + this.minNodeNum = minNodeNum; + this.verbose = verbose; + if (this.maxNodeNum < this.minNodeNum) this.maxNodeNum = this.minNodeNum; + this.reportDF = []; // matrix + } + + findForwardRootEdges(graph: Graph, fromNode: Node): Edge[] { + const result: Edge[] = []; + const nodeMap = graph.nodeMap; + fromNode.edges.forEach((edge) => { + if (this.directed || fromNode.label <= nodeMap[edge.to].label) { + result.push(edge); + } + }); + + return result; + } + + findBackwardEdge( + graph: Graph, + edge1: Edge, + edge2: Edge, + history: History + ): Edge { + if (!this.directed && edge1 === edge2) return null; + const nodeMap = graph.nodeMap; + const edge2To = nodeMap[edge2.to]; + const edge2ToEdges = edge2To.edges; + const edgeLength = edge2ToEdges.length; + for (let i = 0; i < edgeLength; i++) { + const edge = edge2ToEdges[i]; + if (history.hasEdge(edge) || edge.to !== edge1.from) continue; + if (!this.directed) { + if ( + edge1.label < edge.label || + (edge1.label === edge.label && + nodeMap[edge1.to].label <= nodeMap[edge2.to].label) + ) { + return edge; + } + } else { + if ( + nodeMap[edge1.from].label < nodeMap[edge2.to].label || + (nodeMap[edge1.from].label === nodeMap[edge2.to].label && + edge1.label <= edge.label) + ) { + return edge; + } + } + } + return null; + } + + findForwardPureEdges( + graph: Graph, + rightmostEdge: Edge, + minNodeLabel: string, + history: History + ): Edge[] { + const result = []; + const rightmostEdgeToId = rightmostEdge.to; + const edges = graph.nodeMap[rightmostEdgeToId].edges; + const edgeLength = edges.length; + for (let i = 0; i < edgeLength; i++) { + const edge = edges[i]; + const toNode = graph.nodeMap[edge.to]; + if (minNodeLabel <= toNode.label && !history.hasNode(toNode)) { + result.push(edge); + } + } + return result; + } + + findForwardRmpathEdges( + graph: Graph, + rightmostEdge: Edge, + minNodeLabel: string, + history: History + ): Edge[] { + const result = []; + const nodeMap = graph.nodeMap; + const toNodeLabel = nodeMap[rightmostEdge.to].label; + const fromNode = nodeMap[rightmostEdge.from]; + const edges = fromNode.edges; + const edgeLength = edges.length; + for (let i = 0; i < edgeLength; i++) { + const edge = edges[i]; + const newToNodeLabel = nodeMap[edge.to].label; + if ( + rightmostEdge.to === edge.to || + minNodeLabel > newToNodeLabel || + history.hasNode(nodeMap[edge.to]) + ) { + continue; + } + if ( + rightmostEdge.label < edge.label || + (rightmostEdge.label === edge.label && toNodeLabel <= newToNodeLabel) + ) { + result.push(edge); + } + } + return result; + } + + getSupport(projected: PDFS[]): number { + const graphMap: { [key: number]: boolean } = {}; + projected.forEach((pro) => { + if (!graphMap[pro.graphId]) graphMap[pro.graphId] = true; + }); + return Object.keys(graphMap).length; + } + + findMinLabel(obj: Root): { + nodeLabel1?: string; + edgeLabel: string; + nodeLabel2?: string; + } { + let minLabel: { nodeLabel1: string; edgeLabel: string; nodeLabel2: string }; + Object.keys(obj).forEach((nodeEdgeNodeLabel) => { + const { nodeLabel1, edgeLabel, nodeLabel2 } = obj[nodeEdgeNodeLabel]; + if (!minLabel) { + minLabel = { + nodeLabel1, + edgeLabel, + nodeLabel2, + }; + return; + } + if ( + nodeLabel1 < minLabel.nodeLabel1 || + (nodeLabel1 === minLabel.nodeLabel1 && + edgeLabel < minLabel.edgeLabel) || + (nodeLabel1 === minLabel.nodeLabel1 && + edgeLabel === minLabel.edgeLabel && + nodeLabel2 < minLabel.nodeLabel2) + ) { + minLabel = { + nodeLabel1, + edgeLabel, + nodeLabel2, + }; + } + }); + return minLabel; + } + + isMin() { + const dfsCode = this.dfsCode; + if (this.verbose) console.log('isMin checking', dfsCode); + if (dfsCode.dfsEdgeList.length === 1) return true; + const directed = this.directed; + const graph = dfsCode.toGraph(VACANT_GRAPH_ID, directed); + const nodeMap = graph.nodeMap; + const dfsCodeMin = new DFScode(); + const root: Root = {}; + graph.nodes.forEach((node) => { + const forwardEdges = this.findForwardRootEdges(graph, node); + forwardEdges.forEach((edge) => { + const otherNode = nodeMap[edge.to]; + const nodeEdgeNodeLabel = `${node.label}-${edge.label}-${otherNode.label}`; + if (!root[nodeEdgeNodeLabel]) { + root[nodeEdgeNodeLabel] = { + projected: [], + nodeLabel1: node.label, + edgeLabel: edge.label, + nodeLabel2: otherNode.label, + }; + } + const pdfs: PDFS = { + graphId: graph.id, + edge, + preNode: null, + }; + root[nodeEdgeNodeLabel].projected.push(pdfs); + }); + }); + + // 比较 root 中每一项的 nodeEdgeNodeLabel 大小，按照 nodeLabel1、edgeLabe、nodeLabel2 的顺序比较 + const minLabel = this.findMinLabel(root); // line 419 + if (!minLabel) return; + dfsCodeMin.dfsEdgeList.push( + new DFSedge( + 0, + 1, + minLabel.nodeLabel1, + minLabel.edgeLabel, + minLabel.nodeLabel2 + ) + ); + + const projectIsMin = (projected: PDFS[]): any => { + // right most path + const rmpath = dfsCodeMin.buildRmpath(); + const minNodeLabel = + dfsCodeMin.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1; + const maxToC = dfsCodeMin.dfsEdgeList[rmpath[0]].toNode; // node id + + const backwardRoot: Root = {}; + let flag = false; + let newTo = 0; + const end = directed ? -1 : 0; + for (let i = rmpath.length - 1; i > end; i--) { + if (flag) break; + // line 435 + projected.forEach((p) => { + const history = new History(p); + const backwardEdge = this.findBackwardEdge( + graph, + history.edges[rmpath[i]], + history.edges[rmpath[0]], + history + ); + if (backwardEdge) { + // Line 441 + if (!backwardRoot[backwardEdge.label]) { + backwardRoot[backwardEdge.label] = { + projected: [], + edgeLabel: backwardEdge.label, + }; + } + backwardRoot[backwardEdge.label].projected.push({ + graphId: graph.id, + edge: backwardRoot, + preNode: p, + }); + newTo = dfsCodeMin.dfsEdgeList[rmpath[i]].fromNode; + flag = true; + } + }); + } + + if (flag) { + const minBackwardEdgeLabel = this.findMinLabel(backwardRoot); + dfsCodeMin.dfsEdgeList.push( + new DFSedge( + maxToC, + newTo, + VACANT_NODE_LABEL, + minBackwardEdgeLabel.edgeLabel, + VACANT_NODE_LABEL + ) + ); + const idx = dfsCodeMin.dfsEdgeList.length - 1; + if (this.dfsCode.dfsEdgeList[idx] !== dfsCodeMin.dfsEdgeList[idx]) { + return false; + } + return projectIsMin( + backwardRoot[minBackwardEdgeLabel.edgeLabel].projected + ); + } + const forwardRoot: Root = {}; + flag = false; + let newFrom = 0; + projected.forEach((p) => { + const history = new History(p); + const forwardPureEdges = this.findForwardPureEdges( + graph, + history.edges[rmpath[0]], + minNodeLabel, + history + ); + if (forwardPureEdges.length > 0) { + flag = true; + newFrom = maxToC; + forwardPureEdges.forEach((edge) => { + const key = `${edge.label}-${nodeMap[edge.to].label}`; + if (!forwardRoot[key]) { + forwardRoot[key] = { + projected: [], + edgeLabel: edge.label, + nodeLabel2: nodeMap[edge.to].label, + }; + } + forwardRoot[key].projected.push({ + graphId: graph.id, + edge, + preNode: p, + }); + }); + } + }); + + const pathLength = rmpath.length; + for (let i = 0; i < pathLength; i++) { + if (flag) break; + const value = rmpath[i]; + projected.forEach((p) => { + const history = new History(p); + const forwardRmpathEdges = this.findForwardRmpathEdges( + graph, + history.edges[value], + minNodeLabel, + history + ); + if (forwardRmpathEdges.length > 0) { + flag = true; + newFrom = dfsCodeMin.dfsEdgeList[value].fromNode; + forwardRmpathEdges.forEach((edge) => { + const key = `${edge.label}-${nodeMap[edge.to].label}`; + if (!forwardRoot[key]) { + forwardRoot[key] = { + projected: [], + edgeLabel: edge.label, + nodeLabel2: nodeMap[edge.to].label, + }; + } + forwardRoot[key].projected.push({ + graphId: graph.id, + edge, + preNode: p, + }); + }); + } + }); + } + + if (!flag) return true; + + const forwardMinEdgeNodeLabel = this.findMinLabel(forwardRoot); + dfsCodeMin.dfsEdgeList.push( + new DFSedge( + newFrom, + maxToC + 1, + VACANT_NODE_LABEL, + forwardMinEdgeNodeLabel.edgeLabel, + forwardMinEdgeNodeLabel.nodeLabel2 + ) + ); + const idx = dfsCodeMin.dfsEdgeList.length - 1; + if (dfsCode.dfsEdgeList[idx] !== dfsCodeMin.dfsEdgeList[idx]) { + return false; + } + return projectIsMin( + forwardRoot[ + `${forwardMinEdgeNodeLabel.edgeLabel}-${forwardMinEdgeNodeLabel.nodeLabel2}` + ].projected + ); + }; + const key = `${minLabel.nodeLabel1}-${minLabel.edgeLabel}-${minLabel.nodeLabel2}`; + return projectIsMin(root[key].projected); + } + + report() { + if (this.dfsCode.getNodeNum() < this.minNodeNum) return; + this.counter++; + const graph = this.dfsCode.toGraph(this.counter, this.directed); + this.frequentSubgraphs.push(clone(graph)); + } + + subGraphMining(projected: PDFS[]) { + const support = this.getSupport(projected); + if (support < this.minSupport) return; + if (!this.isMin()) return; + this.report(); + + const nodeNum = this.dfsCode.getNodeNum(); + const rmpath = this.dfsCode.buildRmpath(); + const maxToC = this.dfsCode.dfsEdgeList[rmpath[0]].toNode; + const minNodeLabel = + this.dfsCode.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1; + + const forwardRoot: Root = {}; + const backwardRoot: Root = {}; + + projected.forEach((p) => { + const graph = this.graphs[p.graphId]; + const nodeMap = graph.nodeMap; + const history = new History(p); + // backward Line 526 + for (let i = rmpath.length - 1; i >= 0; i--) { + const backwardEdge = this.findBackwardEdge( + graph, + history.edges[rmpath[i]], + history.edges[rmpath[0]], + history + ); + if (backwardEdge) { + const key = `${this.dfsCode.dfsEdgeList[rmpath[i]].fromNode}-${ + backwardEdge.label + }`; + if (!backwardRoot[key]) { + backwardRoot[key] = { + projected: [], + toNodeId: this.dfsCode.dfsEdgeList[rmpath[i]].fromNode, + edgeLabel: backwardEdge.label, + }; + } + backwardRoot[key].projected.push({ + graphId: p.graphId, + edge: backwardEdge, + preNode: p, + }); + } + } + + // pure forward + if (nodeNum >= this.maxNodeNum) return; + const forwardPureEdges = this.findForwardPureEdges( + graph, + history.edges[rmpath[0]], + minNodeLabel, + history + ); + forwardPureEdges.forEach((edge) => { + const key = `${maxToC}-${edge.label}-${nodeMap[edge.to].label}`; + if (!forwardRoot[key]) { + forwardRoot[key] = { + projected: [], + fromNodeId: maxToC, + edgeLabel: edge.label, + nodeLabel2: nodeMap[edge.to].label, + }; + } + forwardRoot[key].projected.push({ + graphId: p.graphId, + edge, + preNode: p, + }); + }); + + // rmpath forward + for (let i = 0; i < rmpath.length; i++) { + const forwardRmpathEdges = this.findForwardRmpathEdges( + graph, + history.edges[rmpath[i]], + minNodeLabel, + history + ); + forwardRmpathEdges.forEach((edge) => { + const key = `${this.dfsCode.dfsEdgeList[rmpath[i]].fromNode}-${ + edge.label + }-${nodeMap[edge.to].label}`; + if (!forwardRoot[key]) { + forwardRoot[key] = { + projected: [], + fromNodeId: this.dfsCode.dfsEdgeList[rmpath[i]].fromNode, + edgeLabel: edge.label, + nodeLabel2: nodeMap[edge.to].label, + }; + } + forwardRoot[key].projected.push({ + graphId: p.graphId, + edge, + preNode: p, + }); + }); + } + }); + + // backward + Object.keys(backwardRoot).forEach((key) => { + const { toNodeId, edgeLabel } = backwardRoot[key]; + this.dfsCode.dfsEdgeList.push( + new DFSedge(maxToC, toNodeId, '-1', edgeLabel, '-1') + ); + this.subGraphMining(backwardRoot[key].projected); + this.dfsCode.dfsEdgeList.pop(); + }); + + // forward + Object.keys(forwardRoot).forEach((key) => { + const { fromNodeId, edgeLabel, nodeLabel2 } = forwardRoot[key]; + this.dfsCode.dfsEdgeList.push( + new DFSedge( + fromNodeId, + maxToC + 1, + VACANT_NODE_LABEL, + edgeLabel, + nodeLabel2 + ) + ); + this.subGraphMining(forwardRoot[key].projected); + this.dfsCode.dfsEdgeList.pop(); + }); + } + + generate1EdgeFrequentSubGraphs() { + const graphs = this.graphs; + const directed = this.directed; + const minSupport = this.minSupport; + const frequentSize1Subgraphs = this.frequentSize1Subgraphs; + const nodeLabelCounter: { [key: string]: number } = {}; + const nodeEdgeNodeCounter: { [key: string]: number } = {}; + // Save the relationship map between each graph and its respective nodes. The key format is "graphKey-nodeType". + const nodeLableCounted: { + [key: string]: { graphKey: string; label: string }; + } = {}; + // Save the relationship map between each graph and its respective edges. The key format is "graphKey-fromNodeType-edgeType-toNodeType". + const nodeEdgeNodeLabelCounted: { + [key: string]: { + graphId: string; + nodeLabel1: string; + edgeLabel: string; + nodeLabel2: string; + }; + } = {}; + Object.keys(graphs).forEach((key) => { + const graph = graphs[Number(key)]; + const nodeMap = graph.nodeMap; + // Traverse the nodes and record the corresponding graph and label for each node in nodeLabelCounted. + graph.nodes.forEach((node, i) => { + const nodeLabel = node.label; + const graphNodeKey = `${key}-${nodeLabel}`; + if (!nodeLableCounted[graphNodeKey]) { + let counter = nodeLabelCounter[nodeLabel] || 0; + counter++; + nodeLabelCounter[nodeLabel] = counter; + } + nodeLableCounted[graphNodeKey] = { + graphKey: key, + label: nodeLabel, + }; + // Traverse all the edges of the node and record the relationship between each graph and its respective edge in nodeEdgeNodeLabelCounted. + node.edges.forEach((edge) => { + let nodeLabel1 = nodeLabel; + let nodeLabel2 = nodeMap[edge.to].label; + if (!directed && nodeLabel1 > nodeLabel2) { + const tmp = nodeLabel2; + nodeLabel2 = nodeLabel1; + nodeLabel1 = tmp; + } + const edgeLabel = edge.label; + + const graphNodeEdgeNodeKey = `${key}-${nodeLabel1}-${edgeLabel}-${nodeLabel2}`; + const nodeEdgeNodeKey = `${nodeLabel1}-${edgeLabel}-${nodeLabel2}`; + + if (!nodeEdgeNodeCounter[nodeEdgeNodeKey]) { + let counter = nodeEdgeNodeCounter[nodeEdgeNodeKey] || 0; + counter++; + nodeEdgeNodeCounter[nodeEdgeNodeKey] = counter; // Line281 + } + nodeEdgeNodeLabelCounted[graphNodeEdgeNodeKey] = { + graphId: key, + nodeLabel1, + edgeLabel, + nodeLabel2, + }; + }); + }); + }); + + // Calculate the frequent nodes. + Object.keys(nodeLabelCounter).forEach((label) => { + const count = nodeLabelCounter[label]; + if (count < minSupport) return; + const g = { nodes: [], edges: [] } as GraphData; + g.nodes.push({ + id: '0', + data: { label }, + }); + frequentSize1Subgraphs.push(g); + // if (minNodeNum <= 1) reportSize1 TODO + }); + + return frequentSize1Subgraphs; + } + + run() { + // -------- First step: _generate_1edge_frequent_subgraphs - Frequent individual nodes. ------- + this.frequentSize1Subgraphs = this.generate1EdgeFrequentSubGraphs(); + + if (this.maxNodeNum < 2) return; + + const graphs = this.graphs; + const directed = this.directed; + + // PDFS array map + const root: Root = {}; + Object.keys(graphs).forEach((graphId: any) => { + const graph = graphs[graphId]; + const nodeMap = graph.nodeMap; + graph.nodes.forEach((node) => { + const forwardRootEdges = this.findForwardRootEdges(graph, node); + forwardRootEdges.forEach((edge) => { + const toNode = nodeMap[edge.to]; + const nodeEdgeNodeLabel = `${node.label}-${edge.label}-${toNode.label}`; + if (!root[nodeEdgeNodeLabel]) { + root[nodeEdgeNodeLabel] = { + projected: [], + nodeLabel1: node.label as string, + edgeLabel: edge.label as string, + nodeLabel2: toNode.label as string, + }; + } + const pdfs: PDFS = { + graphId, + edge, + preNode: null, + }; + root[nodeEdgeNodeLabel].projected.push(pdfs); + }); + }); + }); + + Object.keys(root).forEach((nodeEdgeNodeLabel) => { + const { projected, nodeLabel1, edgeLabel, nodeLabel2 } = + root[nodeEdgeNodeLabel]; + + this.dfsCode.dfsEdgeList.push( + new DFSedge(0, 1, nodeLabel1, edgeLabel, nodeLabel2) + ); + this.subGraphMining(projected); + this.dfsCode.dfsEdgeList.pop(); + }); + } +} + +const formatGraphs = ( + graphs: GraphDataMap, + directed: boolean, + nodeLabelProp: string, + edgeLabelProp: string +): GraphMap => { + const result: { [key: number]: Graph } = {}; + Object.keys(graphs).forEach((key, i) => { + const graph = graphs[key]; + const fGraph = new Graph(i, true, directed); + const nodeIdxMap: { [key: string]: number } = {}; + graph.nodes.forEach((node, j) => { + fGraph.addNode(j, node.data[nodeLabelProp] as string); + nodeIdxMap[node.id] = j; + }); + graph.edges.forEach((edge, k) => { + const sourceIdx = nodeIdxMap[edge.source]; + const targetIdx = nodeIdxMap[edge.target]; + fGraph.addEdge( + -1, + sourceIdx, + targetIdx, + edge.data[edgeLabelProp] as string + ); + }); + if (fGraph && fGraph.getNodeNum()) result[fGraph.id] = fGraph; + }); + return result; +}; + +const toGraphDatas = ( + graphs: Graph[], + nodeLabelProp: string, + edgeLabelProp: string +) => { + const result: GraphData[] = []; + graphs.forEach((graph) => { + const graphData = { nodes: [], edges: [] } as GraphData; + graph.nodes.forEach((node) => { + graphData.nodes.push({ + id: `${node.id}`, + data: { [nodeLabelProp]: node.label }, + }); + }); + graph.edges.forEach((edge) => { + graphData.edges.push({ + id: uniqueId(), + source: `${edge.from}`, + target: `${edge.to}`, + data: { + [edgeLabelProp]: edge.label, + }, + }); + }); + result.push(graphData); + }); + return result; +}; + +interface Props { + graphs: GraphDataMap; // Graph data + minSupport: number; // Algorithm parameter, minimum support count specified based on the number of graphs in the 'graphs' property + directed?: boolean; // Whether the graphs are directed. Default is false. + nodeLabelProp?: string; // Property name for the node type + edgeLabelProp?: string; // Property name for the edge type + minNodeNum?: number; // Minimum number of nodes in each subgraph. Default is 1. + maxNodeNum?: number; // Maximum number of nodes in each subgraph. Default is 4. + top?: number; // Number of top frequent subgraphs to return. Default is 10. + verbose?: boolean; // Whether to display verbose output. +} + +const DEFAULT_LABEL_NAME = 'cluster'; + +/** + * gSpan is a frequent graph mining algorithm used to discover frequent subgraphs in graph data. + * @param params + */ +const gSpan = (params: Props): GraphData[] => { + // ------- Convert the map of graph data, GraphData, into a specific format. ------- + const { + graphs, + directed = false, + nodeLabelProp = DEFAULT_LABEL_NAME, + edgeLabelProp = DEFAULT_LABEL_NAME, + } = params; + const formattedGraphs = formatGraphs( + graphs, + directed, + nodeLabelProp, + edgeLabelProp + ); + const { minSupport, maxNodeNum, minNodeNum, verbose, top } = params; + + // ------- Initialize and execute the algorithm. ------- + const algoParams = { + graphs: formattedGraphs, + minSupport, + maxNodeNum, + minNodeNum, + top, + verbose, + directed, + }; + const calculator = new GSpan(algoParams); + calculator.run(); + + const result = toGraphDatas( + calculator.frequentSubgraphs, + nodeLabelProp, + edgeLabelProp + ); + return result; +}; + +export default gSpan; diff --git a/packages/graph/src/gSpan/struct.ts b/packages/graph/src/gSpan/struct.ts new file mode 100644 index 0000000..1a378a8 --- /dev/null +++ b/packages/graph/src/gSpan/struct.ts @@ -0,0 +1,120 @@ +export const VACANT_EDGE_ID = -1; +export const VACANT_NODE_ID = -1; +export const VACANT_EDGE_LABEL = '-1'; +export const VACANT_NODE_LABEL = '-1'; +export const VACANT_GRAPH_ID = -1; +export const AUTO_EDGE_ID = '-1'; + +export class Edge { + public id: number; + public from: number; + public to: number; + public label: string; + + constructor( + id = VACANT_EDGE_ID, + from = VACANT_NODE_ID, + to = VACANT_NODE_ID, + label = VACANT_EDGE_LABEL + ) { + this.id = id; + this.from = from; + this.to = to; + this.label = label; + } +} + +export class Node { + public id: number; + public from: number; + public to: number; + public label: string; + public edges: Edge[]; + public edgeMap: { [key: number]: Edge }; + + constructor(id = VACANT_NODE_ID, label = VACANT_NODE_LABEL) { + this.id = id; + this.label = label; + this.edges = []; + this.edgeMap = {}; + } + + addEdge(edge: Edge) { + this.edges.push(edge); + this.edgeMap[edge.id] = edge; + } +} + +export class Graph { + public id: number; + public from: number; + public to: number; + public label: string; + public edgeIdAutoIncrease: boolean; + public nodes: Node[]; + public edges: Edge[]; + public nodeMap: { [key: number]: Node }; + public edgeMap: { [key: number]: Edge }; + public nodeLabelMap: { [key: string]: number[] }; // key is label，value is the array of nodes' ids + public edgeLabelMap: { [key: string]: Edge[] }; + private counter: number; // The ID used for generating the graph, incremented automatically. + public directed: boolean; + + constructor( + id = VACANT_NODE_ID, + edgeIdAutoIncrease = true, + directed = false + ) { + this.id = id; + this.edgeIdAutoIncrease = edgeIdAutoIncrease; + this.edges = []; + this.nodes = []; + this.nodeMap = {}; + this.edgeMap = {}; + this.nodeLabelMap = {}; + this.edgeLabelMap = {}; + this.counter = 0; + this.directed = directed; + } + + getNodeNum() { + return this.nodes.length; + } + + addNode(id: number, label: string) { + if (this.nodeMap[id]) return; + const node = new Node(id, label); + this.nodes.push(node); + this.nodeMap[id] = node; + if (!this.nodeLabelMap[label]) this.nodeLabelMap[label] = []; + this.nodeLabelMap[label].push(id); + } + + addEdge(id: number, from: number, to: number, label: string) { + let usingId = id; + if (this.edgeIdAutoIncrease || usingId === undefined) { + usingId = this.counter++; + } + if ( + this.nodeMap[from] && + this.nodeMap[to] && + this.nodeMap[to].edgeMap[usingId] + ) { + return; + } + const edge = new Edge(usingId, from, to, label); + this.edges.push(edge); + this.edgeMap[usingId] = edge; + + this.nodeMap[from].addEdge(edge); + + if (!this.edgeLabelMap[label]) this.edgeLabelMap[label] = []; + this.edgeLabelMap[label].push(edge); + + if (!this.directed) { + const rEdge = new Edge(usingId, to, from, label); + this.nodeMap[to].addEdge(rEdge); + this.edgeLabelMap[label].push(rEdge); + } + } +} diff --git a/packages/graph/src/gaddi.ts b/packages/graph/src/gaddi.ts new file mode 100644 index 0000000..74b19bd --- /dev/null +++ b/packages/graph/src/gaddi.ts @@ -0,0 +1,1444 @@ +import { floydWarshall } from './floydWarshall'; +import { Matrix, Graph, GraphData, IEdge, INode } from './types'; +import gSpan, { EdgeMap, NodeMap } from './gSpan/gSpan'; +import { dijkstra } from './dijkstra'; +import { Graph as GraphCore, ID } from '@antv/graphlib'; + +let uniqueId = 1; + +/** Node pairs map. */ +interface NodePairMap { + /** key is formatted as startNodeIdx-endNodeIdx */ + [key: string]: { + /** ids of the first node */ + start: number; + /** ids of the second node */ + end: number; + /** the shortest path between these two nodes */ + distance: number; + }; +} + +interface LabelMap { + [label: string]: any; +} + +/** Neighbor unit. */ +interface NeighborUnit { + nodeId: ID; + nodeIdx: number; + /** the first one is nodeIdx */ + nodeIdxs: number[]; + neighbors: any[]; // + neighborNum: number; + nodeLabelCountMap: { + [label: string]: { + count: number; + /** Distances array, sortted from small to large */ + dists: number[]; + }; + }; +} + +/** Induced subgraph map of neighbor intersection for node pairs */ +interface InterGraphMap { + /** The key format consists of the indices of node pairs: beginIdx-endIdx, corresponding to nodePairMap */ + [key: string]: GraphData; +} + +/** + * Generates an array of neighbor units for each node in graphData. + * @param {GraphData} graphData - The graph data. + * @param {Matrix[]} spm - The shortest path matrix. + * @param {string} [nodeLabelProp='cluster'] - The property name of the node label. + * @param {number} [k=2] - The number of nearest neighbors to find. + * @returns {NeighborUnit[]} - An array of neighbor units. + * */ +const findKNeighborUnits = ( + graphData: GraphData, + spm: Matrix[], + nodeLabelProp: string = 'cluster', + k: number = 2 +): NeighborUnit[] => { + const units: NeighborUnit[] = []; + const nodes = graphData.nodes; + spm.forEach((row: number[], i) => { + units.push(findKNeighborUnit(nodes, row, i, nodeLabelProp, k)); + }); + return units; +}; +/** + * Finds the neighbor unit for a given node. + * @param {INode[]} nodes - The array of nodes. + * @param {number[]} row - The row of proximity values from the sparse proximity matrix. + * @param {number} i - The index of the node. + * @param {string} nodeLabelProp - The property name of the node label. + * @param {number} k - The number of nearest neighbors. + * @returns {NeighborUnit} - The neighbor unit for the node. + * */ +const findKNeighborUnit = ( + nodes: INode[], + row: number[], + i: number, + nodeLabelProp: string, + k: number +) => { + const unitNodeIdxs = [i]; + const neighbors: INode[] = []; + const labelCountMap: { [key: string]: { count: number; dists: number[] } } = + {}; + row.forEach((v, j) => { + if (v <= k && i !== j) { + unitNodeIdxs.push(j); + neighbors.push(nodes[j]); + const label = nodes[j].data[nodeLabelProp] as string; + if (!labelCountMap[label]) { + labelCountMap[label] = { count: 1, dists: [v] }; + } else { + labelCountMap[label].count++; + labelCountMap[label].dists.push(v); + } + } + }); + // Sort the dists in labelCountMap in ascending order for later use + Object.keys(labelCountMap).forEach((label) => { + labelCountMap[label].dists = labelCountMap[label].dists.sort( + (a, b) => a - b + ); + }); + return { + nodeIdx: i, + nodeId: nodes[i].id, + nodeIdxs: unitNodeIdxs, + neighbors, + neighborNum: unitNodeIdxs.length - 1, + nodeLabelCountMap: labelCountMap, + }; +}; + +/** + * Randomly finds node pairs that satisfy the distance criterion of being less than k. + * @param {number} k - The value of k for k-nearest neighbors. + * @param {number} nodeNum - The number of nodes. + * @param {number} maxNodePairNum - The maximum number of node pairs to be found. + * @param {NeighborUnit[]} kNeighborUnits - The array of neighbor units. + * @param {Matrix[]} spm - The shortest path matrix. + * @returns {NodePairMap} - The map of node pairs. + * */ +const findNodePairsRandomly = ( + k: number, + nodeNum: number, + maxNodePairNum: number, + kNeighborUnits: NeighborUnit[], + spm: Matrix[] +): NodePairMap => { + // Find the pairs randomly for each node + let nodePairNumEachNode = Math.ceil(maxNodePairNum / nodeNum); + const nodePairMap: { + [key: string]: { start: number; end: number; distance: number }; + } = {}; + let foundNodePairCount = 0; + + // Traverse the nodes and find nodePairNumEachNode pairs of nodes, each pair has distance smaller than k. + kNeighborUnits.forEach((unit, i) => { + // If the number of pairs does not reach nodePairNumEachNode, or the loop times is smaller than (2 * nodeNum), keep looping. + let nodePairForICount = 0; + let outerLoopCount = 0; + const neighbors = unit.nodeIdxs; // the first one is the center node + const neighborNum = unit.neighborNum - 1; + const visited: { [key: number]: boolean } = {}; + while (nodePairForICount < nodePairNumEachNode) { + // The other node's indx in the nodes array + let oidx = neighbors[1 + Math.floor(Math.random() * neighborNum)]; + let innerLoopCount = 0; + // If the idx of the other end does not meet the requirement, keep looping to random. + // The requirements are: not the same node, not used, distance smaller than k. + while ( + spm[i][oidx] > k && + (visited[oidx] || + nodePairMap[`${i}-${oidx}`] || + nodePairMap[`${oidx}-${i}`]) + ) { + oidx = Math.floor(Math.random() * nodeNum); + visited[oidx] = true; + innerLoopCount++; + // Break the loop when the loop times is bigger than (2 * nodeNum) to avoid endless loop. + if (innerLoopCount > 2 * nodeNum) break; + } + if (innerLoopCount < 2 * nodeNum) { + // The loop times does not reach the maximum, means the proper other end is found + nodePairMap[`${i}-${oidx}`] = { + start: i, + end: oidx, + distance: spm[i][oidx], + }; + nodePairForICount++; + foundNodePairCount++; + // When the number of the node pairs reaches the maxNodePairNum, return + if (foundNodePairCount >= maxNodePairNum) return nodePairMap; + } + outerLoopCount++; + // Break when the loop times reaches the maximum (2 * nodeNum) to avoid endless loop + if (outerLoopCount > 2 * nodeNum) break; + } + // The node does not have enough node pairs. Update nodePairNumEachNode to find more pairs for other nodes. + if (nodePairForICount < nodePairNumEachNode) { + const gap = nodePairNumEachNode - nodePairForICount; + nodePairNumEachNode = (nodePairNumEachNode + gap) / (nodeNum - i - 1); + } + }); + return nodePairMap; +}; + +/** + * Computes the induced subgraph of the intersection neighbor induced by all node pairs in nodePairMap. + * @param {NodePairMap} nodePairMap - The map of node pairs, where the key is "node1.id-node2.id" and the value is { start: startNodeIdx, end: endNodeIdx, distance }. + * @param {NeighborUnit[]} neighborUnits - The array of neighbor units for each node. + * @param {GraphData} graphData - The original graph data. + * @param {InterGraphMap} cachedInducedGraphMap - The cached results to avoid redundant computations. If a key exists in the cache, the result is already computed and stored. + * @returns {InterGraphMap} - The map of induced subgraphs for each node pair. + * */ +const getIntersectNeighborInducedGraph = ( + nodePairMap: NodePairMap, + neighborUnits: NeighborUnit[], + graphData: GraphData, + cachedInducedGraphMap?: InterGraphMap +): InterGraphMap => { + let usingCachedInducedGraphmap = cachedInducedGraphMap; + const nodes = graphData.nodes; + if (!usingCachedInducedGraphmap) usingCachedInducedGraphmap = {}; + Object.keys(nodePairMap).forEach((key) => { + if (usingCachedInducedGraphmap && usingCachedInducedGraphmap[key]) return; + usingCachedInducedGraphmap[key] = { nodes: [], edges: [] }; + const pair = nodePairMap[key]; + const startUnitNodeIds = neighborUnits[pair.start]?.nodeIdxs; + const endUnitNodeIds = neighborUnits[pair.end]?.nodeIdxs; + // Return empty graph if there are no neighbor units + if (!startUnitNodeIds || !endUnitNodeIds) return; + const endSet = new Set(endUnitNodeIds); + const intersect = startUnitNodeIds.filter((x) => endSet.has(x)); + // Return empty graph if there is no intersection + if (!intersect || !intersect.length) return; + const intersectIdMap: { [key: string]: boolean } = {}; + const intersectLength = intersect.length; + for (let i = 0; i < intersectLength; i++) { + const node = nodes[intersect[i]]; + // Add intersected nodes to the induced subgraph + usingCachedInducedGraphmap[key].nodes.push(node); + intersectIdMap[node.id] = true; + } + graphData.edges.forEach((edge) => { + if (intersectIdMap[edge.source] && intersectIdMap[edge.target]) { + // Add edges to the induced subgraph if both endpoints are in the intersection + usingCachedInducedGraphmap[key].edges.push(edge); + } + }); + }); + return usingCachedInducedGraphmap; +}; + +/** + * Computes the number of matches of the structure on the graph. + * @param {GraphData} graph - The graph data. + * @param {GraphData} structure - The structure to match, currently only supports the simplest structure with two nodes and one edge. + * @param {string} nodeLabelProp - The property name for node labels. + * @param {string} edgeLabelProp - The property name for edge labels. + * @returns {number} - The number of matches. + * */ +const getMatchedCount = ( + graph: GraphData, + structure: GraphData, + nodeLabelProp: string, + edgeLabelProp: string +) => { + const nodeMap: Map = new Map(); + graph.nodes.forEach((node) => { + nodeMap.set(node.id, node); + }); + let count = 0; + if (!structure?.edges?.length || structure?.nodes?.length < 2) return 0; + graph.edges.forEach((e) => { + const sourceLabel = nodeMap.get(e.source).data[nodeLabelProp]; + const targetLabel = nodeMap.get(e.target).data[nodeLabelProp]; + const strNodeLabel1 = structure?.nodes[0].data[nodeLabelProp]; + const strNodeLabel2 = structure?.nodes[1].data[nodeLabelProp]; + const strEdgeLabel = structure?.edges[0].data[edgeLabelProp]; + + if (e.data[edgeLabelProp] !== strEdgeLabel) return; + if ( + (sourceLabel === strNodeLabel1 && targetLabel === strNodeLabel2) || + (sourceLabel === strNodeLabel2 && targetLabel === strNodeLabel1) + ) { + count++; + } + }); + return count; +}; + +/** + * Finds the most representative structure among structures. This structure minimizes the intra-cluster distance and maximizes the inter-cluster distance based on the matchedCountMap grouping. + * @param {Array} matchedCountMap - The matched count map for each structure grouping, in the format { [graphId]: count }. + * @param {number} structureNum - The number of structures, corresponding to the length of matchedCountMap. + * @param {Array} structures - The array of structures. + * @returns {Object} - The most representative structure and its count map. + **/ +const findRepresentStructure = ( + matchedCountMap: { [graphId: string]: number }[], + structureNum: number, + structures: GraphData[] +) => { + let maxOffset = Infinity; + let representClusterType = 0; + for (let i = 0; i < structureNum; i++) { + // Group's map, key is the keys in intGraph, values is the number of matches in structures[i] + const countMapI = matchedCountMap[i]; + // Sort the array bay value, and generate the array of keys: + const sortedGraphKeys = Object.keys(countMapI).sort((a, b) => { + return countMapI[a] - countMapI[b]; + }); + + // 100 graphKeys in total, devided groupKeys into groupNum groups in order + const groupNum = 10; + const clusters: { + graphs: string[]; + totalCount: number; + aveCount: number; + }[] = []; // groupNum items + sortedGraphKeys.forEach((key, j) => { + if (!clusters[j % groupNum]) { + clusters[j % groupNum] = { graphs: [], totalCount: 0, aveCount: 0 }; + } + clusters[j % groupNum].graphs.push(key); + clusters[j % groupNum].totalCount += countMapI[key]; + }); + + // Calculate the distance innerDist between cluster and cluster, and inner distance intraDist inside each cluster. + let aveIntraDist = 0; // The average distances inside the cluster + const aveCounts: number[] = []; // The average of number of matches inside the cluster, will be used to calculate thedistance between clusters. + clusters.forEach((graphsInCluster) => { + // Average count inside the cluster + const aveCount = + graphsInCluster.totalCount / graphsInCluster.graphs.length; + graphsInCluster.aveCount = aveCount; + aveCounts.push(aveCount); + + // Calculate the average distance inside each cluster + let aveIntraPerCluster = 0; + const graphsNum = graphsInCluster.graphs.length; + graphsInCluster.graphs.forEach((graphKey1, j) => { + const graph1Count = countMapI[graphKey1]; + graphsInCluster.graphs.forEach((graphKey2, k) => { + if (j === k) return; + aveIntraPerCluster += Math.abs(graph1Count - countMapI[graphKey2]); + }); + }); + aveIntraPerCluster /= (graphsNum * (graphsNum - 1)) / 2; + aveIntraDist += aveIntraPerCluster; + }); + + aveIntraDist /= clusters.length; + + // Calculate the distance between clusters with the average inside the cluster + let aveInterDist = 0; // 类间间距平均值 + aveCounts.forEach((aveCount1, j) => { + aveCounts.forEach((aveCount2, k) => { + if (j === k) return; + aveInterDist += Math.abs(aveCount1 - aveCount2); + }); + aveInterDist /= (aveCounts.length * (aveCounts.length - 1)) / 2; + }); + + // Find the group with max(average distance between clusters - average dictance inside a cluster). The corresponding structure is the target DS(G). + const offset = aveInterDist - aveIntraDist; + if (maxOffset < offset) { + maxOffset = offset; + representClusterType = i; + } + } + return { + structure: structures[representClusterType], + structureCountMap: matchedCountMap[representClusterType], + }; +}; + +const getNodeMaps = ( + nodes: INode[], + nodeLabelProp: string +): { nodeMap: NodeMap; nodeLabelMap: LabelMap } => { + const nodeMap: NodeMap = {}; + const nodeLabelMap: LabelMap = {}; + nodes.forEach((node, i) => { + nodeMap[node.id] = { idx: i, node, degree: 0, inDegree: 0, outDegree: 0 }; + const label = node.data[nodeLabelProp] as string; + if (!nodeLabelMap[label]) nodeLabelMap[label] = []; + nodeLabelMap[label].push(node); + }); + return { nodeMap, nodeLabelMap }; +}; + +const getEdgeMaps = ( + edges: IEdge[], + edgeLabelProp: string, + nodeMap: NodeMap +): { edgeMap: EdgeMap; edgeLabelMap: LabelMap } => { + const edgeMap: { [key: string]: { idx: number; edge: IEdge } } = {}; + const edgeLabelMap: { [key: string]: IEdge[] } = {}; + edges.forEach((edge, i) => { + edgeMap[`${uniqueId++}`] = { idx: i, edge }; + const label = edge.data[edgeLabelProp] as string; + if (!edgeLabelMap[label]) edgeLabelMap[label] = []; + edgeLabelMap[label].push(edge); + + const sourceNode = nodeMap[edge.source]; + if (sourceNode) { + sourceNode.degree++; + sourceNode.outDegree++; + } + const targetNode = nodeMap[edge.target]; + if (targetNode) { + targetNode.degree++; + targetNode.inDegree++; + } + }); + return { edgeMap, edgeLabelMap }; +}; + +/** + * Generates a map of the shortest paths, where the key is in the format sourceNode.id-targetNode.id, and the value is the shortest path length between the two nodes. + * @param {Array} nodes - The array of nodes. + * @param {Array} spm - The shortest path matrix. + * @param {boolean} directed - Indicates if the graph is directed or not. + * @returns {Object} - The map of shortest paths. + */ +const getSpmMap = ( + nodes: INode[], + spm: number[][], + directed: boolean +): { [key: string]: number } => { + const length = spm.length; + const map: { [key: string]: number } = {}; + spm.forEach((row, i) => { + const start = directed ? 0 : i + 1; + const iId = nodes[i].id; + for (let j = start; j < length; j++) { + if (i === j) continue; + const jId = nodes[j].id; + const dist = row[j]; + map[`${iId}-${jId}`] = dist; + if (!directed) map[`${jId}-${iId}`] = dist; + } + }); + return map; +}; + +/** + * Calculates the NDS distance between a pair of nodes (node1, node2). + * @param {Object} graph - The original graph data. + * @param {Object} node1 - The first node. + * @param {Object} node2 - The second node. + * @param {Object} nodeMap - The map of nodes. + * @param {number} spDist - The shortest path distance between the nodes. + * @param {Array} kNeighborUnits - The array of k-neighbor units. + * @param {Object} structure - The structure graph data. + * @param {string} nodeLabelProp - The node label property. + * @param {string} edgeLabelProp - The edge label property. + * @param {Object} cachedNDSMap - The cached NDS map. + * @param {Object} cachedInterInducedGraph - The cached inter-induced graph map. + * @returns {number} - The NDS distance. + * */ +const getNDSDist = ( + graph: GraphData, + node1: INode, + node2: INode, + nodeMap: NodeMap, + spDist: number, + kNeighborUnits: NeighborUnit[], + structure: GraphData, + nodeLabelProp: string, + edgeLabelProp: string, + cachedNDSMap: { [key: string]: number }, + cachedInterInducedGraph: InterGraphMap +) => { + let usingCachedInterInducedGraph = cachedInterInducedGraph; + const key = `${node1.id}-${node2.id}`; + if (cachedNDSMap && cachedNDSMap[key]) return cachedNDSMap[key]; + let interInducedGraph = usingCachedInterInducedGraph + ? usingCachedInterInducedGraph[key] + : undefined; + // If there is no cached intersected induced graph, calculate it + if (!interInducedGraph) { + const pairMap: NodePairMap = { + [key]: { + start: nodeMap[node1.id].idx as number, + end: nodeMap[node2.id].idx as number, + distance: spDist, + }, + }; + + usingCachedInterInducedGraph = getIntersectNeighborInducedGraph( + pairMap, + kNeighborUnits, + graph, + usingCachedInterInducedGraph + ); + interInducedGraph = usingCachedInterInducedGraph[key]; + } + + return getMatchedCount( + interInducedGraph, + structure, + nodeLabelProp, + edgeLabelProp + ); +}; + +/** + * Calculates the degrees of nodes in the pattern and stores them in the minPatternNodeLabelDegreeMap. + * @param {Object} minPatternNodeLabelDegreeMap - The map for storing the minimum degrees of nodes in the pattern. + * @param {string} neighborLabel - The label of the neighbor. + * @param {Object} patternNodeMap - The map of nodes in the pattern. + * @param {Object} patternNodeLabelMap - The map of node labels in the pattern. + * @returns {Object} - The minimum degrees of nodes in the pattern. + * */ +const stashPatternNodeLabelDegreeMap = ( + minPatternNodeLabelDegreeMap: { + [key: string]: { + degree: number; + inDegree: number; + outDegree: number; + }; + }, + neighborLabel: string, + patternNodeMap: NodeMap, + patternNodeLabelMap: LabelMap +) => { + let minPatternNodeLabelDegree = + minPatternNodeLabelDegreeMap[neighborLabel]?.degree; + let minPatternNodeLabelInDegree = + minPatternNodeLabelDegreeMap[neighborLabel]?.inDegree; + let minPatternNodeLabelOutDegree = + minPatternNodeLabelDegreeMap[neighborLabel]?.outDegree; + + if (minPatternNodeLabelDegreeMap[neighborLabel] === undefined) { + minPatternNodeLabelDegree = Infinity; + minPatternNodeLabelInDegree = Infinity; + minPatternNodeLabelOutDegree = Infinity; + patternNodeLabelMap[neighborLabel].forEach( + (patternNodeWithLabel: INode) => { + const patternNodeDegree = + patternNodeMap[patternNodeWithLabel.id].degree; + if (minPatternNodeLabelDegree > patternNodeDegree) { + minPatternNodeLabelDegree = patternNodeDegree; + } + const patternNodeInDegree = + patternNodeMap[patternNodeWithLabel.id].inDegree; + if (minPatternNodeLabelInDegree > patternNodeInDegree) { + minPatternNodeLabelInDegree = patternNodeInDegree; + } + const patternNodeOutDegree = + patternNodeMap[patternNodeWithLabel.id].outDegree; + if (minPatternNodeLabelOutDegree > patternNodeOutDegree) { + minPatternNodeLabelOutDegree = patternNodeOutDegree; + } + } + ); + minPatternNodeLabelDegreeMap[neighborLabel] = { + degree: minPatternNodeLabelDegree, + inDegree: minPatternNodeLabelInDegree, + outDegree: minPatternNodeLabelOutDegree, + }; + } + + return { + minPatternNodeLabelDegree, + minPatternNodeLabelInDegree, + minPatternNodeLabelOutDegree, + }; +}; + +/** + * GADDI Pattern Match. + * @param graph The graphlib structure storing the original data + * @param pattern The pattern graph data to search + * @param directed Whether it is a directed graph, false by default + * @param k k-nearest-neighbors + * @param length length + * @param nodeLabelProp The field name for the label (clustering info) in the node data, 'cluster' by default + * @param edgeLabelProp The field name for the label (clustering info) in the edge data, 'cluster' by default + */ +export const GADDI = ( + graph: Graph, + pattern: GraphData, + directed: boolean = false, + k: number, + length: number, + nodeLabelProp: string = 'cluster', + edgeLabelProp: string = 'cluster' +): GraphData[] => { + const graphData = { + nodes: graph.getAllNodes(), + edges: graph.getAllEdges(), + }; + if (!graph || !graphData.nodes) return; + const patternGraph = new GraphCore(pattern); + let usingLength = length; + let usingK = k; + + // Three steps: + // 0. Pre-processing: number of nodes/edges, adjacency matrix, shortest path distance matrix + // 1. Processing original graph data in 5 steps + // 2. Matching + + // console.log("----- stage-pre: preprocessing -------"); + + // -------- Step 0: Pre-processing: number of nodes/edges, adjacency matrix, shortest path distance matrix------- + const nodeNum = graphData.nodes.length; + if (!nodeNum) return; + // console.log("----- stage-pre.1: calc shortest path matrix for graph -------"); + const spm = floydWarshall(graph, directed); + // console.log( + // "----- stage-pre.2: calc shortest path matrix for pattern -------" + // ); + const patternSpm = floydWarshall(patternGraph, directed); + // console.log( + // "----- stage-pre.3: calc shortest path matrix map for graph -------" + // ); + const spmMap = getSpmMap(graphData.nodes, spm, directed); + // console.log( + // "----- stage-pre.4: calc shortest path matrix map for pattern -------" + // ); + const patternSpmMap = getSpmMap(pattern.nodes, patternSpm, directed); + + // console.log("----- stage-pre.5: establish maps -------"); + // A node map is created to map nodes to their IDs, facilitating fast retrieval in subsequent operations. + const { nodeMap, nodeLabelMap } = getNodeMaps(graphData.nodes, nodeLabelProp); + const { nodeMap: patternNodeMap, nodeLabelMap: patternNodeLabelMap } = + getNodeMaps(pattern.nodes, nodeLabelProp); + + // Calculate the node degrees + getEdgeMaps(graphData.edges, edgeLabelProp, nodeMap); + + const { edgeLabelMap: patternEdgeLabelMap } = getEdgeMaps( + pattern.edges, + edgeLabelProp, + patternNodeMap + ); + + // If the length is not assigned, calculate the radius (max shortest path distance) of the pattern + let patternSpmSpread: number[] = []; + patternSpm?.forEach((row) => { + patternSpmSpread = patternSpmSpread.concat(row); + }); + if (!usingLength) usingLength = Math.max(...patternSpmSpread, 2); + if (!usingK) usingK = usingLength; + + // console.log("----- stage-pre.6: calc k neighbor units -------"); + // Calculate the k-nearest-neighbor collection for each node + const kNeighborUnits = findKNeighborUnits( + graphData, + spm, + nodeLabelProp, + usingK + ); + const patternKNeighborUnits = findKNeighborUnits( + pattern, + patternSpm, + nodeLabelProp, + usingK + ); + + // console.log( + // "----- stage0: going to processing graph and find intersect neighbor induced graphs -------" + // ); + + // console.log("----- stage0.1: going to select random node pairs -------"); + // -------- Step 1: Processing the original graph data------- + + // 1.1. Find 100 node pairs in max, the distance smaller than Length and k + // When the number of nodes in graphData is smaller then 20, 100 node pairs are not able to be found. Only find no more than n(n-1)/2 pairs. + const maxNodePairNum = Math.min(100, (nodeNum * (nodeNum - 1)) / 2); + const nodePairsMap = findNodePairsRandomly( + usingK, + nodeNum, + maxNodePairNum, + kNeighborUnits, + spm + ); + + // console.log( + // "----- stage0.2: going to calculate intersect neighbor induced graphs -------" + // ); + // 1.2. Generate the intersected induced neighbor subgraph for each node pairs, formatted as {'beginNodeIdx-endNodeIdx': {nodes: [], edges: []}} + let intGMap = getIntersectNeighborInducedGraph( + nodePairsMap, + kNeighborUnits, + graphData + ); + // 1.3. Calculate the top frequent sub structures with 3-4 edges in ISIntG, with gSpan(frequent graph mining) algorithm + const top = 10; + const params = { + graphs: intGMap, + nodeLabelProp, + edgeLabelProp, + minSupport: 1, + minNodeNum: 1, + maxNodeNum: 4, + directed, + }; + + // console.log( + // "----- stage1: (gSpan) going to find frequent structure dsG -------" + // ); + // console.log("----- stage1.1: going to run gSpan -------"); + // suppose that the generated sub structure has only one edge + const freStructures = gSpan(params).slice(0, top); + // structureNum can be less than top + const structureNum = freStructures.length; + + // 1.4. Calculate the number of matches of each induced subgraph in intGMap + const matchedCountMap: { [key: string]: number }[] = []; + freStructures.forEach((structure, i) => { + matchedCountMap[i] = {}; + Object.keys(intGMap).forEach((key) => { + const graph = intGMap[key]; + const subStructureCount = getMatchedCount( + graph, + structure, + nodeLabelProp, + edgeLabelProp + ); + matchedCountMap[i][key] = subStructureCount; + }); + }); + + // console.log( + // "----- stage1.1: going to find the most represent strucutre -------" + // ); + + // 1.5. For each sub structure, group the induced sub graph in initGMap accroding to the matches number. There will be structureNum groups. + // Calculate the intra and inner distances of each group, find the max and min groups. These groups' corresponding su structure will be selected as the representing structure DS(G) + const { structure: dsG, structureCountMap: ndsDist } = findRepresentStructure( + matchedCountMap, + structureNum, + freStructures + ); + + // -------- Step 2: Matching------- + // 2.1 找到从 Q 中的一个节点作为起始节点，寻找 G 中的匹配。这个其实节点的标签可以在 G 中找到最多的节点 + let beginPNode = pattern.nodes[0]; + let candidates: INode[] = []; + let label = pattern.nodes[0]?.data[nodeLabelProp]; + let maxNodeNumWithSameLabel = -Infinity; + pattern.nodes.forEach((node) => { + const pLabel = node.data[nodeLabelProp] as string; + const nodesWithSameLabel = nodeLabelMap[pLabel]; + if (nodesWithSameLabel?.length > maxNodeNumWithSameLabel) { + maxNodeNumWithSameLabel = nodesWithSameLabel.length; + candidates = nodesWithSameLabel; + label = pLabel; + beginPNode = node; + } + }); + + // console.log("----- stage2: going to find candidates -------"); + + // Global caching is used to avoid redundant calculations. + const minPatternNodeLabelDegreeMap = {}; // Key is label, value is the minimum degree of the nodes with label + let patternIntGraphMap: InterGraphMap = {}; + const patternNDSDist: { [key: string]: number } = {}; // key is node.id-node.id + const patternNDSDistMap: { [key: string]: number[] } = {}; // key is node.id-label2, value nds array is sortted from large to small + // 2.2.2 For the k nodes with another label in Q, calculate the shortest path distance to the node and the NDS distance + const patternSpDist: { [key: string]: number[] } = {}; + const patternSpDistBack: { [key: string]: number[] } = {}; + Object.keys(patternNodeLabelMap).forEach((label2, j) => { + patternSpDist[label2] = []; + if (directed) { + patternSpDistBack[label2] = []; + } + let maxDist = -Infinity; + const patternNodesWithLabel2 = patternNodeLabelMap[label2]; + const patternNodePairMap: { + [key: string]: { + start: number; + end: number; + distance: number; + }; + } = {}; + patternNodesWithLabel2.forEach((nodeWithLabel2: INode) => { + const dist = patternSpmMap[`${beginPNode.id}-${nodeWithLabel2.id}`]; + dist && patternSpDist[label2].push(dist); + if (maxDist < dist) maxDist = dist; + patternNodePairMap[`${beginPNode.id}-${nodeWithLabel2.id}`] = { + start: 0, + end: patternNodeMap[nodeWithLabel2.id].idx, + distance: dist, + }; + if (directed) { + const distBack = patternSpmMap[`${nodeWithLabel2.id}-${beginPNode.id}`]; + distBack && patternSpDistBack[label2].push(distBack); + } + }); + + // spDist[label2] sortted from small to large + patternSpDist[label2] = patternSpDist[label2].sort((a, b) => a - b); + if (directed) { + patternSpDistBack[label2] = patternSpDistBack[label2].sort( + (a, b) => a - b + ); + } + + // Calculate NDS distances from all the nodes in Q with label2 to beginPNode + // The intersected neighbor induced subgraph from label2 nodes to beginPNode: + // key: node1.id-node2.id + patternIntGraphMap = getIntersectNeighborInducedGraph( + patternNodePairMap, + patternKNeighborUnits, + pattern, + patternIntGraphMap + ); + // array of NDS distances from beginNode in pattern to the current node with label2, the corresponding relations does not matter + let currentPatternNDSDistArray: number[] = []; + Object.keys(patternNodePairMap).forEach((key) => { + if (patternNDSDist[key]) { + currentPatternNDSDistArray.push(patternNDSDist[key]); + // If it is cached, no need to calculate again + return; + } + const patternIntGraph = patternIntGraphMap[key]; + patternNDSDist[key] = getMatchedCount( + patternIntGraph, + dsG, + nodeLabelProp, + edgeLabelProp + ); + currentPatternNDSDistArray.push(patternNDSDist[key]); + }); + + // Sortted by currentPatternNDSDist from large to small + currentPatternNDSDistArray = currentPatternNDSDistArray.sort( + (a, b) => b - a + ); + patternNDSDistMap[`${beginPNode.id}-${label2}`] = + currentPatternNDSDistArray; + + if (label2 === label) return; + + const candidatesNum = candidates?.length || 0; + for (let m = candidatesNum - 1; m >= 0; m--) { + const cNode = candidates[m]; + + // prune1: If the number of nodes with label2 in the kNeighborUnits of node cNode in candidates is less than the number of nodes with label2 in the pattern, remove it. + const graphNeighborUnit = kNeighborUnits[nodeMap[cNode.id].idx]; + const graphNeighborUnitCountMap = + graphNeighborUnit.nodeLabelCountMap[label2]; + const patternLabel2Num = patternNodeLabelMap[label2].length; + if ( + !graphNeighborUnitCountMap || + graphNeighborUnitCountMap.count < patternLabel2Num + ) { + candidates.splice(m, 1); + continue; + } + + // prune2: If the shortest path from node cNode in candidates to any node with label2 in the kNeighborUnits is greater than patternSpDist[label2], remove it. + // The prune2 rule states that for each candidate, we compare the top spDist[label2].length shortest path distances from the candidate to any node with label2 in the kNeighborUnits, + // in order of their magnitude, with the corresponding values in patternSpDist[label2]. If we encounter a value where G > Q, we remove that candidate. + let prune2Invalid = false; + for (let n = 0; n < patternLabel2Num; n++) { + if (graphNeighborUnitCountMap.dists[n] > patternSpDist[label2][n]) { + prune2Invalid = true; + break; + } + } + if (prune2Invalid) { + candidates.splice(m, 1); + continue; + } + + // prune3: If the NDS distance from node cNode in candidates to any node with label2 in the kNeighborUnits is less than patternNDSDist[beginNode.id-label2], remove it. + // TODO：prune3: compare currentPatternNDSDistArray and currentNDSDist + + // Calculate the NDS distances from all the nodes in label2 in G to the cNode + // All the intersected neighbor induced subgraph of nodes with label2 to cNode: + const cNodePairMap: { + [key: string]: { start: number; end: number; distance: number }; + } = {}; + graphNeighborUnit.neighbors.forEach((neighborNode) => { + const dist = spmMap[`${cNode.id}-${neighborNode.id}`]; + cNodePairMap[`${cNode.id}-${neighborNode.id}`] = { + start: nodeMap[cNode.id].idx, + end: nodeMap[neighborNode.id].idx, + distance: dist, + }; + }); + // Update intGMap + intGMap = getIntersectNeighborInducedGraph( + cNodePairMap, + kNeighborUnits, + graphData, + intGMap + ); + // NDS distance from candidate to the neighbor nodes with label2, key is node.id-node.id + let currentNDSDistArray: number[] = []; + Object.keys(cNodePairMap).forEach((key) => { + if (ndsDist[key]) { + currentNDSDistArray.push(ndsDist[key]); + return; // If it is cached, there is no need to calculate it again. + } + const intGraph = intGMap[key]; + ndsDist[key] = getMatchedCount( + intGraph, + dsG, + nodeLabelProp, + edgeLabelProp + ); + currentNDSDistArray.push(ndsDist[key]); + }); + + // Sortted by currentNDSDistArray from large to small + currentNDSDistArray = currentNDSDistArray.sort((a, b) => b - a); + + let prune3Invalid = false; + for (let n = 0; n < patternLabel2Num; n++) { + if (currentNDSDistArray[n] < currentPatternNDSDistArray[n]) { + prune3Invalid = true; + break; + } + } + if (prune3Invalid) { + candidates.splice(m, 1); + continue; + } + } + }); + + const candidateGraphs: GraphData[] = []; + + // console.log( + // "----- stage3: going to splice neighbors for each candidate graph -------" + // ); + + // After filtering the candidates, generate a Length-neighbor induced subgraph with each candidate as the center. + // In the induced subgraph, remove points that cannot be matched on Q: labels that do not exist in Q, + // and labels where the maximum shortest distance from other labels to the candidate is not in accordance with Q and the NDS distance is not in accordance with Q. + candidates?.forEach((candidate, ci) => { + const nodeIdx = nodeMap[candidate.id].idx; + const lengthNeighborUnit = findKNeighborUnit( + graphData.nodes, + spm[nodeIdx], + nodeIdx, + nodeLabelProp, + usingLength + ); + + const neighborNodes = lengthNeighborUnit.neighbors; + + // Remove the neighbor node which has no probability to find the matches + const neighborNum = neighborNodes.length; + let unmatched = false; + for (let i = neighborNum - 1; i >= 0; i--) { + // If, after pruning, the number of nodes that meet the criteria is too small, it indicates that the candidate graph cannot be matched. + if (neighborNodes.length + 1 < pattern.nodes.length) { + unmatched = true; + return; + } + const neighborNode = neighborNodes[i]; + const neighborLabel = neighborNode.data[nodeLabelProp] as string; + // prune1: If the label of neighbor nodes does not exist in pattern, remove the node + if ( + !patternNodeLabelMap[neighborLabel] || + !patternNodeLabelMap[neighborLabel].length + ) { + neighborNodes.splice(i, 1); + continue; + } + + // prune2: If the shortest path from the neighbor node to the candidate is longer than the maximum shortest path length from any node with the same label as the neighbor node to beginPNode, remove this node. + // prune2.1: If there is no distance record from this label to beginPNode, it means that there are no other nodes with this label on the pattern (possibly beginPNode has this label). + if ( + !patternSpDist[neighborLabel] || + !patternSpDist[neighborLabel].length + ) { + neighborNodes.splice(i, 1); + continue; + } + + const key = `${candidate.id}-${neighborNode.id}`; + + // prune2.2 + const distToCandidate = spmMap[key]; + let idx = patternSpDist[neighborLabel].length - 1; + const maxDistWithLabelInPattern = patternSpDist[neighborLabel][idx]; // patternSpDist[neighborLabel] has been sortted from small to large + if (distToCandidate > maxDistWithLabelInPattern) { + neighborNodes.splice(i, 1); + continue; + } + + if (directed) { + const keyBack = `${neighborNode.id}-${candidate.id}`; + const distFromCandidate = spmMap[keyBack]; + idx = patternSpDistBack[neighborLabel].length - 1; + const maxBackDistWithLabelInPattern = + patternSpDistBack[neighborLabel][idx]; + if (distFromCandidate > maxBackDistWithLabelInPattern) { + neighborNodes.splice(i, 1); + continue; + } + } + + // prune3: If the NDS distance from the neighbor node to the candidate is smaller than the minimum NDS distance from any node with the same label as the neighbor node to beginPNode, remove this node. + const ndsToCandidate = ndsDist[key] + ? ndsDist[key] + : getNDSDist( + graphData, + candidate, + neighborNode, + nodeMap, + distToCandidate, + kNeighborUnits, + dsG, + nodeLabelProp, + edgeLabelProp, + ndsDist, + intGMap + ); + const patternKey = `${beginPNode.id}-${neighborLabel}`; + const minNdsWithLabelInPattern = + patternNDSDistMap[patternKey][patternNDSDistMap[patternKey].length - 1]; // patternNDSDist[key] exists for sure + if (ndsToCandidate < minNdsWithLabelInPattern) { + neighborNodes.splice(i, 1); + continue; + } + + // prune4: If the degree of the neighbor node is less than the minimum degree of nodes with the same label in the pattern, remove this node. + const { + minPatternNodeLabelDegree, + minPatternNodeLabelInDegree, + minPatternNodeLabelOutDegree, + } = stashPatternNodeLabelDegreeMap( + minPatternNodeLabelDegreeMap, + neighborLabel, + patternNodeMap, + patternNodeLabelMap + ); + + if (nodeMap[neighborNode.id].degree < minPatternNodeLabelDegree) { + neighborNodes.splice(i, 1); + continue; + } + } + + // The number of nodes satisfies the matching requirement (not less than the number of nodes in the pattern). Now, we will filter the related edges. + if (!unmatched) { + candidateGraphs.push({ + nodes: [candidate].concat(neighborNodes), + }); + } + }); + + // console.log( + // "----- stage4: going to splice edges and neighbors for each candidate graph -------" + // ); + + const { length: undirectedLengthsToBeginPNode } = dijkstra( + patternGraph, + beginPNode.id, + false + ); + + let undirectedLengthsToBeginPNodeLabelMap: { [key: string]: number[] } = {}; + if (directed) { + Object.keys(undirectedLengthsToBeginPNode).forEach((nodeId) => { + const nodeLabel = patternNodeMap[nodeId].node.data[nodeLabelProp]; + if (!undirectedLengthsToBeginPNodeLabelMap[nodeLabel]) { + undirectedLengthsToBeginPNodeLabelMap[nodeLabel] = [ + undirectedLengthsToBeginPNode[nodeId], + ]; + } else { + undirectedLengthsToBeginPNodeLabelMap[nodeLabel].push( + undirectedLengthsToBeginPNode[nodeId] + ); + } + }); + Object.keys(undirectedLengthsToBeginPNodeLabelMap).forEach((pLabel) => { + undirectedLengthsToBeginPNodeLabelMap[pLabel].sort((a, b) => a - b); + }); + } else { + undirectedLengthsToBeginPNodeLabelMap = patternSpDist; + } + + // Only nodes in andidateGraphs now. Filter edges: + const candidateGraphNum = candidateGraphs.length; + for (let i = candidateGraphNum - 1; i >= 0; i--) { + const candidateGraph = candidateGraphs[i]; + const candidate = candidateGraph.nodes[0]; + + const candidateNodeLabelCountMap: { [key: string]: number } = {}; + const candidateNodeMap: { + [key: string]: { + idx: number; + node: INode; + degree: number; + inDegree: number; + outDegree: number; + }; + } = {}; + candidateGraph.nodes.forEach((node, q) => { + candidateNodeMap[node.id] = { + idx: q, + node, + degree: 0, + inDegree: 0, + outDegree: 0, + }; + const cNodeLabel = node.data[nodeLabelProp] as string; + if (!candidateNodeLabelCountMap[cNodeLabel]) { + candidateNodeLabelCountMap[cNodeLabel] = 1; + } else candidateNodeLabelCountMap[cNodeLabel]++; + }); + + // Generate the induced subgraph of G based on the nodes in candidates and neighborNodes. + // In other words, include the edges from graphData where both endpoints are in candidateGraph.nodes into candidateEdges. + const candidateEdges: IEdge[] = []; + const edgeLabelCountMap: { [key: string]: number } = {}; + graphData.edges.forEach((edge) => { + if (candidateNodeMap[edge.source] && candidateNodeMap[edge.target]) { + candidateEdges.push(edge); + if (!edgeLabelCountMap[edge.data[edgeLabelProp] as string]) { + edgeLabelCountMap[edge.data[edgeLabelProp] as string] = 1; + } else edgeLabelCountMap[edge.data[edgeLabelProp] as string]++; + candidateNodeMap[edge.source].degree++; + candidateNodeMap[edge.target].degree++; + candidateNodeMap[edge.source].outDegree++; + candidateNodeMap[edge.target].inDegree++; + } + }); + + // prune: If the number of occurrences of a specific edgeLabel in candidateGraph is fewer than in the pattern, remove that graph. + const pattenrEdgeLabelNum = Object.keys(patternEdgeLabelMap).length; + let prunedByEdgeLabel = false; + for (let e = 0; e < pattenrEdgeLabelNum; e++) { + const label = Object.keys(patternEdgeLabelMap)[e]; + if ( + !edgeLabelCountMap[label] || + edgeLabelCountMap[label] < patternEdgeLabelMap[label].length + ) { + prunedByEdgeLabel = true; + break; + } + } + if (prunedByEdgeLabel) { + candidateGraphs.splice(i, 1); + continue; + } + + // Traverse candidateEdges to filter the edges + let candidateEdgeNum = candidateEdges.length; + + // prune: If the edge number is too small, remove the graph + if (candidateEdgeNum < pattern.edges.length) { + candidateGraphs.splice(i, 1); + break; + } + let candidateGraphInvalid = false; + for (let e = candidateEdgeNum - 1; e >= 0; e--) { + const edge = candidateEdges[e]; + const edgeLabel = edge.data[edgeLabelProp] as string; + const patternEdgesWithLabel = patternEdgeLabelMap[edgeLabel]; + + // prune 1: If the label of an edge does not exist in the edge labels of the pattern, remove that edge. + if (!patternEdgesWithLabel || !patternEdgesWithLabel.length) { + edgeLabelCountMap[edgeLabel]--; + // If the count of a certain label decreases and the number of edges with that label becomes insufficient, remove that graph. + if ( + patternEdgesWithLabel && + edgeLabelCountMap[edgeLabel] < patternEdgesWithLabel.length + ) { + candidateGraphInvalid = true; + break; + } + candidateEdges.splice(e, 1); + candidateNodeMap[edge.source].degree--; + candidateNodeMap[edge.target].degree--; + candidateNodeMap[edge.source].outDegree--; + candidateNodeMap[edge.target].inDegree--; + continue; + } + + // prune 2: If the triplet relationship of the edge label + both endpoint labels cannot be found in the pattern, remove that edge. + const sourceLabel = + candidateNodeMap[edge.source].node.data[nodeLabelProp]; + const targetLabel = + candidateNodeMap[edge.target].node.data[nodeLabelProp]; + + let edgeMatched = false; + patternEdgesWithLabel.forEach((patternEdge: IEdge) => { + const patternSource = patternNodeMap[patternEdge.source].node; + const patternTarget = patternNodeMap[patternEdge.target].node; + if ( + patternSource.data[nodeLabelProp] === sourceLabel && + patternTarget.data[nodeLabelProp] === targetLabel + ) { + edgeMatched = true; + } + if ( + !directed && + patternSource.data[nodeLabelProp] === targetLabel && + patternTarget.data[nodeLabelProp] === sourceLabel + ) { + edgeMatched = true; + } + }); + if (!edgeMatched) { + edgeLabelCountMap[edgeLabel]--; + // If the count of a label decreases and the number of edges with that label is insufficient, remove that graph. + if ( + patternEdgesWithLabel && + edgeLabelCountMap[edgeLabel] < patternEdgesWithLabel.length + ) { + candidateGraphInvalid = true; + break; + } + candidateEdges.splice(e, 1); + candidateNodeMap[edge.source].degree--; + candidateNodeMap[edge.target].degree--; + candidateNodeMap[edge.source].outDegree--; + candidateNodeMap[edge.target].inDegree--; + continue; + } + } + + // prune2: During the process of deleting edges, if it is found that there are too few edges or too few edge labels, remove that graph. + if (candidateGraphInvalid) { + candidateGraphs.splice(i, 1); + continue; + } + + candidateGraph.edges = candidateEdges; + + const { length: lengthsToCandidate } = dijkstra( + new GraphCore(candidateGraph), + candidateGraph.nodes[0].id, + false // The calculation of path length here is used to determine connectivity, so an undirected graph is used. + ); + Object.keys(lengthsToCandidate) + .reverse() + .forEach((targetId) => { + if (targetId === candidateGraph.nodes[0].id || candidateGraphInvalid) { + return; + } + // prune4: The pruning described above may result in the neighbor subgraph becoming disconnected. Remove the nodes in the neighbor subgraph that are currently not connected to the candidate (first node). + if (lengthsToCandidate[targetId] === Infinity) { + const targetNodeLabel = candidateNodeMap[targetId].node.data[ + nodeLabelProp + ] as string; + candidateNodeLabelCountMap[targetNodeLabel]--; + if ( + candidateNodeLabelCountMap[targetNodeLabel] < + patternNodeLabelMap[targetNodeLabel].length + ) { + candidateGraphInvalid = true; + return; + } + const idx = candidateGraph.nodes.indexOf( + candidateNodeMap[targetId].node + ); + candidateGraph.nodes.splice(idx, 1); + candidateNodeMap[targetId] = undefined; + return; + } + // prune5: After the edge pruning, it is possible that there are nodes with excessively long shortest paths (compared to the maximum shortest distance from nodes with the same label in the pattern to beginNode). Remove these nodes. + const nLabel = nodeMap[targetId].node.data[nodeLabelProp]; + if ( + !undirectedLengthsToBeginPNodeLabelMap[nLabel] || + !undirectedLengthsToBeginPNodeLabelMap[nLabel].length || + lengthsToCandidate[targetId] > + undirectedLengthsToBeginPNodeLabelMap[nLabel][ + undirectedLengthsToBeginPNodeLabelMap[nLabel].length - 1 + ] + ) { + const targetNodeLabel = candidateNodeMap[targetId].node.data[ + nodeLabelProp + ] as string; + candidateNodeLabelCountMap[targetNodeLabel]--; + if ( + candidateNodeLabelCountMap[targetNodeLabel] < + patternNodeLabelMap[targetNodeLabel].length + ) { + candidateGraphInvalid = true; + return; + } + const idx = candidateGraph.nodes.indexOf( + candidateNodeMap[targetId].node + ); + candidateGraph.nodes.splice(idx, 1); + candidateNodeMap[targetId] = undefined; + } + }); + + if (candidateGraphInvalid) { + candidateGraphs.splice(i, 1); + continue; + } + + let degreeChanged = true; + let loopCount = 0; + while (degreeChanged && !candidateGraphInvalid) { + degreeChanged = false; + + // degree of candidate is not enough, remove it + const condition = directed + ? candidateNodeMap[candidate.id].degree < + patternNodeMap[beginPNode.id].degree || + candidateNodeMap[candidate.id].inDegree < + patternNodeMap[beginPNode.id].inDegree || + candidateNodeMap[candidate.id].outDegree < + patternNodeMap[beginPNode.id].outDegree + : candidateNodeMap[candidate.id].degree < + patternNodeMap[beginPNode.id].degree; + if (condition) { + candidateGraphInvalid = true; + break; + } + // the number of candidate label is not enough, remove it + if ( + candidateNodeLabelCountMap[candidate.data[nodeLabelProp] as string] < + patternNodeLabelMap[candidate.data[nodeLabelProp] as string].length + ) { + candidateGraphInvalid = true; + break; + } + + // prune6: remove the nodes with small degree + const currentCandidateNodeNum = candidateGraph.nodes.length; + for (let o = currentCandidateNodeNum - 1; o >= 0; o--) { + const cgNode = candidateGraph.nodes[o]; + const nodeDegree = candidateNodeMap[cgNode.id].degree; + const nodeInDegree = candidateNodeMap[cgNode.id].inDegree; + const nodeOutDegree = candidateNodeMap[cgNode.id].outDegree; + const cNodeLabel = cgNode.data[nodeLabelProp] as string; + + const { + minPatternNodeLabelDegree, + minPatternNodeLabelInDegree, + minPatternNodeLabelOutDegree, + } = stashPatternNodeLabelDegreeMap( + minPatternNodeLabelDegreeMap, + cNodeLabel, + patternNodeMap, + patternNodeLabelMap + ); + + const deleteCondition = directed + ? nodeDegree < minPatternNodeLabelDegree || + nodeInDegree < minPatternNodeLabelInDegree || + nodeOutDegree < minPatternNodeLabelOutDegree + : nodeDegree < minPatternNodeLabelDegree; + if (deleteCondition) { + candidateNodeLabelCountMap[cgNode.data[nodeLabelProp] as string]--; + // 节点 label 个数不足 + if ( + candidateNodeLabelCountMap[cgNode.data[nodeLabelProp] as string] < + patternNodeLabelMap[cgNode.data[nodeLabelProp] as string].length + ) { + candidateGraphInvalid = true; + break; + } + candidateGraph.nodes.splice(o, 1); + candidateNodeMap[cgNode.id] = undefined; + degreeChanged = true; + } + } + if (candidateGraphInvalid || (!degreeChanged && loopCount !== 0)) break; + // After the prune5 node pruning, remove the edges whose endpoints are no longer in candidateGraph. + candidateEdgeNum = candidateEdges.length; + for (let y = candidateEdgeNum - 1; y >= 0; y--) { + const cedge = candidateEdges[y]; + if ( + !candidateNodeMap[cedge.source] || + !candidateNodeMap[cedge.target] + ) { + candidateEdges.splice(y, 1); + const edgeLabel = cedge.data[edgeLabelProp] as string; + edgeLabelCountMap[edgeLabel]--; + if (candidateNodeMap[cedge.source]) { + candidateNodeMap[cedge.source].degree--; + candidateNodeMap[cedge.source].outDegree--; + } + if (candidateNodeMap[cedge.target]) { + candidateNodeMap[cedge.target].degree--; + candidateNodeMap[cedge.target].inDegree--; + } + // Label number is not enough + if ( + patternEdgeLabelMap[edgeLabel] && + edgeLabelCountMap[edgeLabel] < patternEdgeLabelMap[edgeLabel].length + ) { + candidateGraphInvalid = true; + break; + } + degreeChanged = true; + } + } + loopCount++; + } + + if (candidateGraphInvalid) { + candidateGraphs.splice(i, 1); + continue; + } + + // prune: If there are too few nodes/edges or too few node/edge labels, remove this graph. + if ( + candidateGraphInvalid || + candidateGraph.nodes.length < pattern.nodes.length || + candidateEdges.length < pattern.edges.length + ) { + candidateGraphs.splice(i, 1); + continue; + } + } + + // At this point, multiple candidateGraphs have been generated, and there may be duplicates. + + // console.log( + // "----- stage5: going to splice dulplicated candidate graphs -------" + // ); + + // Remove identical subgraphs in candidateGraphs by using the node-node-edgeLabel of edges as the key and the count of such edges as the value for matching. + let currentLength = candidateGraphs.length; + for (let i = 0; i <= currentLength - 1; i++) { + const cg1 = candidateGraphs[i]; + const cg1EdgeMap: { [key: string]: number } = {}; // [node1.id-node2.id-edge.label]: count + cg1.edges.forEach((edge) => { + const key = `${edge.source}-${edge.target}-${edge.data.label}`; + if (!cg1EdgeMap[key]) cg1EdgeMap[key] = 1; + else cg1EdgeMap[key]++; + }); + + for (let j = currentLength - 1; j > i; j--) { + const cg2 = candidateGraphs[j]; + const cg2EdgeMap: { [key: string]: number } = {}; // [node1.id-node2.id-edge.label]: count + cg2.edges.forEach((edge) => { + const key = `${edge.source}-${edge.target}-${edge.data.label}`; + if (!cg2EdgeMap[key]) cg2EdgeMap[key] = 1; + else cg2EdgeMap[key]++; + }); + + let same = true; + if (Object.keys(cg2EdgeMap).length !== Object.keys(cg1EdgeMap).length) { + same = false; + } else { + Object.keys(cg1EdgeMap).forEach((key) => { + if (cg2EdgeMap[key] !== cg1EdgeMap[key]) same = false; + }); + } + if (same) { + candidateGraphs.splice(j, 1); + } + } + currentLength = candidateGraphs.length; + } + + return candidateGraphs; +}; diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index 6af8072..ba47401 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -1,10 +1,11 @@ -export * from "./pageRank"; -export * from "./findPath"; -export * from "./louvain"; -export * from "./iLouvain"; -export * from "./k-core"; -export * from "./floydWarshall"; -export * from "./bfs"; -export * from "./dfs"; -export * from "./cosine-similarity" -export * from "./nodes-cosine-similarity"; \ No newline at end of file +export * from './pageRank'; +export * from './findPath'; +export * from './louvain'; +export * from './iLouvain'; +export * from './k-core'; +export * from './floydWarshall'; +export * from './bfs'; +export * from './dfs'; +export * from './cosine-similarity'; +export * from './nodes-cosine-similarity'; +export * from './gaddi'; diff --git a/packages/graph/src/structs/linked-list.ts b/packages/graph/src/structs/linked-list.ts index 89abeee..dd111a2 100644 --- a/packages/graph/src/structs/linked-list.ts +++ b/packages/graph/src/structs/linked-list.ts @@ -28,7 +28,7 @@ export default class LinkedList { return true; } return false; - }; + } constructor(comparator?: Function) { this.head = null; @@ -126,7 +126,7 @@ export default class LinkedList { } let currentNode = this.head; while (currentNode) { - //find by callback first + // find by callback first if (callback && callback(currentNode.value)) { return currentNode; } diff --git a/packages/graph/src/types.ts b/packages/graph/src/types.ts index d05b75f..5315abd 100644 --- a/packages/graph/src/types.ts +++ b/packages/graph/src/types.ts @@ -1,4 +1,4 @@ -import { Edge, Graph as IGraph, Node, PlainObject } from "@antv/graphlib"; +import { Edge, Graph as IGraph, Node, PlainObject } from '@antv/graphlib'; // 数据集中属性/特征值分布的map export interface KeyValueMap { @@ -34,7 +34,11 @@ export type Matrix = number[]; export interface IAlgorithmCallbacks { enter?: (param: { current: NodeID; previous: NodeID }) => void; leave?: (param: { current: NodeID; previous?: NodeID }) => void; - allowTraversal?: (param: { previous?: NodeID; current?: NodeID; next: NodeID }) => boolean; + allowTraversal?: (param: { + previous?: NodeID; + current?: NodeID; + next: NodeID; + }) => boolean; } export type NodeID = string | number; @@ -42,5 +46,13 @@ export type NodeID = string | number; export type NodeSimilarity = Node & { data: { cosineSimilarity?: number; - } -} \ No newline at end of file + }; +}; + +export type GraphData = { + nodes: Node[]; + edges?: Edge[]; +}; + +export type INode = Node; +export type IEdge = Edge; From 8c2e18da6295bb54a3215bf1e6e6fd0923aa7e1a Mon Sep 17 00:00:00 2001 From: Cee Date: Mon, 18 Sep 2023 11:19:56 +0800 Subject: [PATCH 08/15] feat: algorithm MST(Minimum Spanning Tree) (#72) * feat: v5 algorithm mst * test: mst unit test * fix: fix lint * fix: fix lint * fix: graph get related edge api * chore: translate chinese annotation * chore: remove the unnecessary var --- __tests__/unit/mst.spec.ts | 113 ++++++++++++++++++ package.json | 2 +- packages/graph/src/cosine-similarity.ts | 2 +- packages/graph/src/index.ts | 1 + packages/graph/src/mst.ts | 106 ++++++++++++++++ packages/graph/src/nodes-cosine-similarity.ts | 6 +- packages/graph/src/structs/binary-heap.ts | 87 ++++++++++++++ packages/graph/src/structs/union-find.ts | 44 +++++++ packages/graph/src/types.ts | 8 +- packages/graph/src/utils.ts | 23 ++-- 10 files changed, 374 insertions(+), 18 deletions(-) create mode 100644 __tests__/unit/mst.spec.ts create mode 100644 packages/graph/src/mst.ts create mode 100644 packages/graph/src/structs/binary-heap.ts create mode 100644 packages/graph/src/structs/union-find.ts diff --git a/__tests__/unit/mst.spec.ts b/__tests__/unit/mst.spec.ts new file mode 100644 index 0000000..492cd1f --- /dev/null +++ b/__tests__/unit/mst.spec.ts @@ -0,0 +1,113 @@ +import { minimumSpanningTree } from "../../packages/graph/src"; +import { Graph } from "@antv/graphlib"; + +const data = { + nodes: [ + { + id: 'A', + data: {}, + }, + { + id: 'B', + data: {}, + }, + { + id: 'C', + data: {}, + }, + { + id: 'D', + data: {}, + }, + { + id: 'E', + data: {}, + }, + { + id: 'F', + data: {}, + }, + { + id: 'G', + data: {}, + }, + ], + edges: [ + { + id: 'edge1', + source: 'A', + target: 'B', + data: { + weight: 1, + } + }, + { + id: 'edge2', + source: 'B', + target: 'C', + data: { + weight: 1, + } + }, + { + id: 'edge3', + source: 'A', + target: 'C', + data: { + weight: 2, + } + }, + { + id: 'edge4', + source: 'D', + target: 'A', + data: { + weight: 3, + } + }, + { + id: 'edge5', + source: 'D', + target: 'E', + data: { + weight: 4, + } + }, + { + id: 'edge6', + source: 'E', + target: 'F', + data: { + weight: 2, + } + }, + { + id: 'edge7', + source: 'F', + target: 'D', + data: { + weight: 3, + } + }, + ], +}; +const graph = new Graph(data); +describe('minimumSpanningTree', () => { + it('test kruskal algorithm', () => { + let result = minimumSpanningTree(graph, 'weight'); + let totalWeight = 0; + for (let edge of result) { + totalWeight += edge.data.weight; + } + expect(totalWeight).toEqual(10); + }); + + it('test prim algorithm', () => { + let result = minimumSpanningTree(graph, 'weight', 'prim'); + let totalWeight = 0; + for (let edge of result) { + totalWeight += edge.data.weight; + } + expect(totalWeight).toEqual(10); + }); +}); diff --git a/package.json b/package.json index 8a6e34a..f3e823a 100644 --- a/package.json +++ b/package.json @@ -21,7 +21,7 @@ "build:ci": "pnpm -r run build:ci", "prepare": "husky install", "test": "jest", - "test_one": "jest ./__tests__/unit/nodes-cosine-similarity.spec.ts", + "test_one": "jest ./__tests__/unit/mst.spec.ts", "coverage": "jest --coverage", "build:site": "vite build", "deploy": "gh-pages -d site/dist", diff --git a/packages/graph/src/cosine-similarity.ts b/packages/graph/src/cosine-similarity.ts index 554623b..9f5b5f7 100644 --- a/packages/graph/src/cosine-similarity.ts +++ b/packages/graph/src/cosine-similarity.ts @@ -24,4 +24,4 @@ export const cosineSimilarity = ( // Calculate the cosine similarity between the item vector and the target element vector const cosineSimilarity = norm2Product ? dot / norm2Product : 0; return cosineSimilarity; -} +}; diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index ba47401..6413f1b 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -9,3 +9,4 @@ export * from './dfs'; export * from './cosine-similarity'; export * from './nodes-cosine-similarity'; export * from './gaddi'; +export * from './mst'; \ No newline at end of file diff --git a/packages/graph/src/mst.ts b/packages/graph/src/mst.ts new file mode 100644 index 0000000..17eae0b --- /dev/null +++ b/packages/graph/src/mst.ts @@ -0,0 +1,106 @@ +import UnionFind from './structs/union-find'; +import MinBinaryHeap from './structs/binary-heap'; +import { Graph, IEdge, IMSTAlgorithm, IMSTAlgorithmOpt } from './types'; +import { clone } from '@antv/util'; + +/** +Calculates the Minimum Spanning Tree (MST) of a graph using the Prim's algorithm.The MST is a subset of edges that forms a tree connecting all nodes with the minimum possible total edge weight. +@param graph - The graph for which the MST needs to be calculated. +@param weightProps - Optional. The property name in the edge data object that represents the weight of the edge.If provided, the algorithm will consider the weight of edges based on this property.If not provided, the algorithm will assume all edges have a weight of 0. +@returns An array of selected edges that form the Minimum Spanning Tree (MST) of the graph. +*/ +const primMST: IMSTAlgorithm = (graph, weightProps?) => { + const selectedEdges: IEdge[] = []; + const nodes = graph.getAllNodes(); + const edges = graph.getAllEdges(); + if (nodes.length === 0) { + return selectedEdges; + } + // From the first node + const currNode = nodes[0]; + const visited = new Set(); + visited.add(currNode); + // Using binary heap to maintain the weight of edges from other nodes that have joined the node + const compareWeight = (a: IEdge, b: IEdge) => { + if (weightProps) { + a.data; + return (a.data[weightProps] as number) - (b.data[weightProps] as number); + } + return 0; + }; + const edgeQueue = new MinBinaryHeap(compareWeight); + + graph.getRelatedEdges(currNode.id, 'both').forEach((edge) => { + edgeQueue.insert(edge); + }); + while (!edgeQueue.isEmpty()) { + // Select the node with the least edge weight between the added node and the added node + const currEdge: IEdge = edgeQueue.delMin(); + const source = currEdge.source; + const target = currEdge.target; + if (visited.has(source) && visited.has(target)) continue; + selectedEdges.push(currEdge); + if (!visited.has(source)) { + visited.add(source); + graph.getRelatedEdges(source, 'both').forEach((edge) => { + edgeQueue.insert(edge); + }); + } + if (!visited.has(target)) { + visited.add(target); + graph.getRelatedEdges(target, 'both').forEach((edge) => { + edgeQueue.insert(edge); + }); + } + } + return selectedEdges; +}; + +/** +Calculates the Minimum Spanning Tree (MST) of a graph using the Kruskal's algorithm.The MST is a subset of edges that forms a tree connecting all nodes with the minimum possible total edge weight. +@param graph - The graph for which the MST needs to be calculated. +@param weightProps - Optional. The property name in the edge data object that represents the weight of the edge.If provided, the algorithm will consider the weight of edges based on this property.If not provided, the algorithm will assume all edges have a weight of 0. +@returns An array of selected edges that form the Minimum Spanning Tree (MST) of the graph. +*/ +const kruskalMST: IMSTAlgorithm = (graph, weightProps?) => { + const selectedEdges: IEdge[] = []; + const nodes = graph.getAllNodes(); + const edges = graph.getAllEdges(); + if (nodes.length === 0) { + return selectedEdges; + } + // If you specify weight, all edges are sorted by weight from smallest to largest + const weightEdges = clone(edges); + if (weightProps) { + weightEdges.sort((a: IEdge, b: IEdge) => { + return (a.data[weightProps] as number) - (b.data[weightProps] as number); + }); + } + const disjointSet = new UnionFind(nodes.map((n) => n.id)); + // Starting with the edge with the least weight, if the two nodes connected by this edge are not in the same connected component in graph G, the edge is added. + while (weightEdges.length > 0) { + const curEdge = weightEdges.shift(); + const source = curEdge.source; + const target = curEdge.target; + if (!disjointSet.connected(source, target)) { + selectedEdges.push(curEdge); + disjointSet.union(source, target); + } + } + return selectedEdges; +}; + +/** +Calculates the Minimum Spanning Tree (MST) of a graph using either Prim's or Kruskal's algorithm.The MST is a subset of edges that forms a tree connecting all nodes with the minimum possible total edge weight. +@param graph - The graph for which the MST needs to be calculated. +@param weightProps - Optional. The property name in the edge data object that represents the weight of the edge.If provided, the algorithm will consider the weight of edges based on this property.If not provided, the algorithm will assume all edges have a weight of 0. +@param algo - Optional. The algorithm to use for calculating the MST. Can be either 'prim' for Prim's algorithm, 'kruskal' for Kruskal's algorithm, or undefined to use the default algorithm (Kruskal's algorithm). +@returns An array of selected edges that form the Minimum Spanning Tree (MST) of the graph. +*/ +export const minimumSpanningTree = (graph: Graph, weightProps?: string, algo?: 'prim' | 'kruskal' | undefined): IEdge[] => { + const algos: IMSTAlgorithmOpt = { + 'prim': primMST, + 'kruskal': kruskalMST, + }; + return (algo && algos[algo](graph, weightProps)) || kruskalMST(graph, weightProps); +}; diff --git a/packages/graph/src/nodes-cosine-similarity.ts b/packages/graph/src/nodes-cosine-similarity.ts index 9ef5201..9687326 100644 --- a/packages/graph/src/nodes-cosine-similarity.ts +++ b/packages/graph/src/nodes-cosine-similarity.ts @@ -21,8 +21,8 @@ export const nodesCosineSimilarity = ( allCosineSimilarity: number[], similarNodes: NodeSimilarity[], } => { - const similarNodes = clone(nodes.filter(node => node.id !== seedNode.id)); - const seedNodeIndex = nodes.findIndex(node => node.id === seedNode.id); + const similarNodes = clone(nodes.filter((node) => node.id !== seedNode.id)); + const seedNodeIndex = nodes.findIndex((node) => node.id === seedNode.id); // Collection of all node properties const properties = getAllProperties(nodes); // One-hot feature vectors for all node properties @@ -40,4 +40,4 @@ export const nodesCosineSimilarity = ( // Sort the returned nodes according to cosine similarity similarNodes.sort((a: NodeSimilarity, b: NodeSimilarity) => b.data.cosineSimilarity - a.data.cosineSimilarity); return { allCosineSimilarity, similarNodes }; -} +}; diff --git a/packages/graph/src/structs/binary-heap.ts b/packages/graph/src/structs/binary-heap.ts new file mode 100644 index 0000000..4637552 --- /dev/null +++ b/packages/graph/src/structs/binary-heap.ts @@ -0,0 +1,87 @@ + +export default class MinBinaryHeap { + list: T[]; + + compareFn: (a?: T, b?: T) => number; + + constructor(compareFn: (a: T, b: T) => number) { + this.compareFn = compareFn || (() => 0); + this.list = []; + } + + getLeft(index: number) { + return 2 * index + 1; + } + + getRight(index: number) { + return 2 * index + 2; + } + + getParent(index: number) { + if (index === 0) { + return null; + } + return Math.floor((index - 1) / 2); + } + + isEmpty() { + return this.list.length <= 0; + } + + top() { + return this.isEmpty() ? undefined : this.list[0]; + } + + delMin() { + const top = this.top(); + const bottom = this.list.pop(); + if (this.list.length > 0) { + this.list[0] = bottom; + this.moveDown(0); + } + return top; + } + + insert(value: T) { + if (value !== null) { + this.list.push(value); + const index = this.list.length - 1; + this.moveUp(index); + return true; + } + return false; + } + + moveUp(index: number) { + let i = index; + let parent = this.getParent(i); + while (i && i > 0 && this.compareFn(this.list[parent], this.list[i]) > 0) { + // swap + const tmp = this.list[parent]; + this.list[parent] = this.list[i]; + this.list[i] = tmp; + i = parent; + parent = this.getParent(i); + } + } + + moveDown(index: number) { + let element = index; + const left = this.getLeft(index); + const right = this.getRight(index); + const size = this.list.length; + if (left !== null && left < size && this.compareFn(this.list[element], this.list[left]) > 0) { + element = left; + } else if ( + right !== null && + right < size && + this.compareFn(this.list[element], this.list[right]) > 0 + ) { + element = right; + } + if (index !== element) { + [this.list[index], this.list[element]] = [this.list[element], this.list[index]]; + this.moveDown(element); + } + } +} diff --git a/packages/graph/src/structs/union-find.ts b/packages/graph/src/structs/union-find.ts new file mode 100644 index 0000000..2758b5e --- /dev/null +++ b/packages/graph/src/structs/union-find.ts @@ -0,0 +1,44 @@ +/** + * Disjoint set to support quick union + */ +export default class UnionFind { + count: number; + + parent: { [key: number | string]: number | string }; + + constructor(items: (number | string)[]) { + this.count = items.length; + this.parent = {}; + for (const i of items) { + this.parent[i] = i; + } + } + + // find the root of the item + find(item: (number | string)) { + let resItem = item; + while (this.parent[resItem] !== resItem) { + resItem = this.parent[resItem]; + } + return resItem; + } + + union(a: (number | string), b: (number | string)) { + const rootA = this.find(a); + const rootB = this.find(b); + if (rootA === rootB) return; + // make the element with smaller root the parent + if (rootA < rootB) { + if (this.parent[b] !== b) this.union(this.parent[b], a); + this.parent[b] = this.parent[a]; + } else { + if (this.parent[a] !== a) this.union(this.parent[a], b); + this.parent[a] = this.parent[b]; + } + } + + // Determine that A and B are connected + connected(a: (number | string), b: (number | string)) { + return this.find(a) === this.find(b); + } +} diff --git a/packages/graph/src/types.ts b/packages/graph/src/types.ts index 5315abd..d4aeeee 100644 --- a/packages/graph/src/types.ts +++ b/packages/graph/src/types.ts @@ -1,6 +1,6 @@ import { Edge, Graph as IGraph, Node, PlainObject } from '@antv/graphlib'; -// 数据集中属性/特征值分布的map +// Map of attribute / eigenvalue distribution in dataset export interface KeyValueMap { [key: string]: any[]; } @@ -56,3 +56,9 @@ export type GraphData = { export type INode = Node; export type IEdge = Edge; + +export type IMSTAlgorithm = (graph: Graph, weightProps?: string) => IEdge[]; +export interface IMSTAlgorithmOpt { + 'prim': IMSTAlgorithm; + 'kruskal': IMSTAlgorithm; +} \ No newline at end of file diff --git a/packages/graph/src/utils.ts b/packages/graph/src/utils.ts index 78d5103..4a884fb 100644 --- a/packages/graph/src/utils.ts +++ b/packages/graph/src/utils.ts @@ -12,17 +12,17 @@ export const getAllProperties = (nodes: Node[]) => { export const getAllKeyValueMap = (dataList: PlainObject[], involvedKeys?: string[], uninvolvedKeys?: string[]) => { let keys: string[] = []; - // 指定了参与计算的keys时，使用指定的keys + // Use the specified keys when the keys participating in the calculation is specified if (involvedKeys?.length) { keys = involvedKeys; } else { - // 未指定抽取的keys时，提取数据中所有的key + // When the extracted keys is not specified, all key in the data is extracted dataList.forEach((data) => { keys = keys.concat(Object.keys(data)); }); keys = uniq(keys); } - // 获取所有值非空的key的value数组 + // Get the value array of all key with non-null values const allKeyValueMap: KeyValueMap = {}; keys.forEach((key) => { const value: unknown[] = []; @@ -40,19 +40,17 @@ export const getAllKeyValueMap = (dataList: PlainObject[], involvedKeys?: string }; export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvolvedKeys?: string[]) => { - // 获取数据中所有的属性/特征及其对应的值 + // Get all attributes / features in the data and their corresponding values const allKeyValueMap = getAllKeyValueMap(dataList, involvedKeys, uninvolvedKeys); const oneHotCode: unknown[][] = []; if (!Object.keys(allKeyValueMap).length) { return oneHotCode; } - - // 获取所有的属性/特征值 + // Get all attribute / feature values const allValue = Object.values(allKeyValueMap); - // 是否所有属性/特征的值都是数值型 + // Whether the values of all attributes / features are numerical const isAllNumber = allValue.every((value) => value.every((item) => (typeof (item) === 'number'))); - - // 对数据进行one-hot编码 + // One-hot encode the data dataList.forEach((data, index) => { let code: unknown[] = []; Object.keys(allKeyValueMap).forEach((key) => { @@ -60,11 +58,11 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol const allKeyValue = allKeyValueMap[key]; const valueIndex = allKeyValue.findIndex((value) => keyValue === value); const subCode = []; - // 如果属性/特征所有的值都能转成数值型，不满足分箱，则直接用值（todo: 为了收敛更快，需做归一化处理） + // If all the values of the attribute / feature can be converted to numerical type and do not satisfy the box division, then use the value directly (todo: normalization is needed for faster convergence) if (isAllNumber) { subCode.push(keyValue); } else { - // 进行one-hot编码 + // Encode one-hot for (let i = 0; i < allKeyValue.length; i++) { if (i === valueIndex) { subCode.push(1); @@ -78,4 +76,5 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol oneHotCode[index] = code; }); return oneHotCode; -}; \ No newline at end of file +}; + From f26eb24f8993b95fb033a00750a36bad40beeb42 Mon Sep 17 00:00:00 2001 From: Cee Date: Mon, 18 Sep 2023 18:58:58 +0800 Subject: [PATCH 09/15] feat: v5 algorithm connected components (#73) * feat: v5 algorithm connected components * test: unit test of connected components * fix: fix lint * fix: move 'dataTransformer' into test dir * fix: remove unnecessary type --- __tests__/data/connected-test-data.json | 51577 +++++++++++++++++++ __tests__/unit/connected-component.spec.ts | 135 + __tests__/utils/data.ts | 19 + package.json | 2 +- packages/graph/src/connected-component.ts | 104 + packages/graph/src/index.ts | 3 +- packages/graph/src/utils.ts | 1 - 7 files changed, 51838 insertions(+), 3 deletions(-) create mode 100644 __tests__/data/connected-test-data.json create mode 100644 __tests__/unit/connected-component.spec.ts create mode 100644 __tests__/utils/data.ts create mode 100644 packages/graph/src/connected-component.ts diff --git a/__tests__/data/connected-test-data.json b/__tests__/data/connected-test-data.json new file mode 100644 index 0000000..5640c01 --- /dev/null +++ b/__tests__/data/connected-test-data.json @@ -0,0 +1,51577 @@ +{ + "nodes": [ + { + "id": "1", + "olabel": "Kuperman_M", + "size": 10, + "x": 26.22602890442184, + "y": 33.48766498594824, + "index": 0, + "vy": -0.014258006060071719, + "vx": -0.013787795594269568 + }, + { + "id": "2", + "olabel": "Acebron_J", + "size": 10, + "x": 762.9747722969743, + "y": 1076.5655474780835, + "index": 1, + "vy": 0.02444286459421935, + "vx": 0.012612873834233911 + }, + { + "id": "3", + "olabel": "Bonilla_L", + 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b/__tests__/unit/connected-component.spec.ts @@ -0,0 +1,135 @@ +import { getConnectedComponents } from "../../packages/graph/src"; +import { Graph } from "@antv/graphlib"; +import connectedTestData from '../data/connected-test-data.json'; +import { dataTransformer } from "../utils/data"; + +const data = { + nodes: [ + { + id: 'A', + data: {}, + }, + { + id: 'B', + data: {}, + }, + { + id: 'C', + data: {}, + }, + { + id: 'D', + data: {}, + }, + { + id: 'E', + data: {}, + }, + { + id: 'F', + data: {}, + }, + { + id: 'G', + data: {}, + }, + { + id: 'H', + data: {}, + }, + ], + edges: [ + { + id: 'edge-0', + source: 'A', + target: 'B', + data: {}, + }, + { + id: 'edge-1', + source: 'B', + target: 'C', + data: {}, + }, + { + id: 'edge-2', + source: 'A', + target: 'C', + data: {}, + }, + { + id: 'edge-3', + source: 'D', + target: 'A', + data: {}, + }, + { + id: 'edge-4', + source: 'D', + target: 'E', + data: {}, + }, + { + id: 'edge-5', + source: 'E', + target: 'F', + data: {}, + }, + { + id: 'edge-6', + source: 'F', + target: 'D', + data: {}, + }, + { + id: 'edge-7', + source: 'G', + target: 'H', + data: {}, + }, + { + id: 'edge-8', + source: 'H', + target: 'G', + data: {}, + }, + ], +}; +const graph = new Graph(data); + +describe('find connected components', () => { + it('detect strongly connected components in undirected graph', () => { + let result = getConnectedComponents(graph, false); + expect(result.length).toEqual(2); + expect(result[0].map((node) => node.id).sort()).toEqual(['A', 'B', 'C', 'D', 'E', 'F']); + expect(result[1].map((node) => node.id).sort()).toEqual(['G', 'H']); + }); + + it('detect strongly connected components in directed graph', () => { + let result = getConnectedComponents(graph, true); + expect(result.length).toEqual(5); + expect(result[3].map((node) => node.id).sort()).toEqual(['D', 'E', 'F']); + expect(result[4].map((node) => node.id).sort()).toEqual(['G', 'H']); + }); + + + it('test connected components detection performance using large graph', () => { + const testData = dataTransformer(connectedTestData); + testData.nodes.forEach((node) => { + node.data.label = node.data.olabel; + node.data.degree = 0; + data.edges.forEach((edge) => { + if (edge.source === node.id || edge.target === node.id) { + (node.data.degree as number) += 1; + } + }); + }); + const graph = new Graph(testData); + let directedComps = getConnectedComponents(graph, true); + let undirectedComps = getConnectedComponents(graph, false); + expect(directedComps.length).toEqual(1589); + expect(undirectedComps.length).toEqual(396); + + }); + +}); diff --git a/__tests__/utils/data.ts b/__tests__/utils/data.ts new file mode 100644 index 0000000..9c131d7 --- /dev/null +++ b/__tests__/utils/data.ts @@ -0,0 +1,19 @@ +import { NodeID, INode, IEdge } from "../../packages/graph/src/types"; +/** + * Convert the old version of the data format to the new version + * @param data old data + * @return {{nodes:INode[],edges:IEdge[]}} new data + */ +export const dataTransformer = (data: { nodes: { id: NodeID, [key: string]: any }[], edges: { source: NodeID, target: NodeID, [key: string]: any }[] }): { nodes: INode[], edges: IEdge[] } => { + const { nodes, edges } = data; + return { + nodes: nodes.map((n) => { + const { id, ...rest } = n; + return { id, data: rest }; + }), + edges: edges.map((e, i) => { + const { id, source, target, ...rest } = e; + return { id: id ? id : `edge-${i}`, target, source, data: rest }; + }), + }; +}; diff --git a/package.json b/package.json index f3e823a..e8f2ba9 100644 --- a/package.json +++ b/package.json @@ -74,4 +74,4 @@ "access": "public", "registry": "https://registry.npmjs.org" } -} +} \ No newline at end of file diff --git a/packages/graph/src/connected-component.ts b/packages/graph/src/connected-component.ts new file mode 100644 index 0000000..ce00e0f --- /dev/null +++ b/packages/graph/src/connected-component.ts @@ -0,0 +1,104 @@ +import { Graph, INode, NodeID } from './types'; +/** + * Generate all connected components for an undirected graph + * @param graph + */ +export const detectConnectedComponents = (graph: Graph): INode[][] => { + const nodes = graph.getAllNodes(); + const allComponents: INode[][] = []; + const visited: { [key: NodeID]: boolean } = {}; + const nodeStack: INode[] = []; + const getComponent = (node: INode) => { + nodeStack.push(node); + visited[node.id] = true; + const neighbors = graph.getNeighbors(node.id); + for (let i = 0; i < neighbors.length; ++i) { + const neighbor = neighbors[i].id; + if (!visited[neighbor]) { + const targetNode = nodes.filter((node) => node.id === neighbor); + if (targetNode.length > 0) { + getComponent(targetNode[0]); + } + } + } + }; + for (let i = 0; i < nodes.length; i++) { + const node = nodes[i]; + if (!visited[node.id]) { + // For DFS traversal of undirected graphs, a connected component is obtained after each call + getComponent(node); + const component = []; + while (nodeStack.length > 0) { + component.push(nodeStack.pop()); + } + allComponents.push(component); + } + } + return allComponents; +}; + +/** + * Tarjan's Algorithm O(|V|+|E|) + * For directed graph only + * a directed graph is said to be strongly connected if "every vertex is reachable from every other vertex". + * refer: http://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithm + * @param graph + * @return a list of strongly connected components + */ +export const detectStrongConnectComponents = (graph: Graph): INode[][] => { + const nodes = graph.getAllNodes(); + const nodeStack: INode[] = []; + // Assist to determine whether it is already in the stack to reduce the search overhead + const inStack: { [key: NodeID]: boolean } = {}; + const indices: { [key: NodeID]: number } = {}; + const lowLink: { [key: NodeID]: number } = {}; + const allComponents: INode[][] = []; + let index = 0; + const getComponent = (node: INode) => { + // Set the depth index for v to the smallest unused index + indices[node.id] = index; + lowLink[node.id] = index; + index += 1; + nodeStack.push(node); + inStack[node.id] = true; + const relatedEdges = graph.getRelatedEdges(node.id, "out"); + for (let i = 0; i < relatedEdges.length; i++) { + const targetNodeID = relatedEdges[i].target; + if (!indices[targetNodeID] && indices[targetNodeID] !== 0) { + const targetNode = nodes.filter((node) => node.id === targetNodeID); + if (targetNode.length > 0) { + getComponent(targetNode[0]); + } + // tree edge + lowLink[node.id] = Math.min(lowLink[node.id], lowLink[targetNodeID]); + } else if (inStack[targetNodeID]) { + // back edge, target node is in the current SCC + lowLink[node.id] = Math.min(lowLink[node.id], indices[targetNodeID]); + } + } + // If node is a root node, generate an SCC + if (lowLink[node.id] === indices[node.id]) { + const component = []; + while (nodeStack.length > 0) { + const tmpNode = nodeStack.pop(); + inStack[tmpNode.id] = false; + component.push(tmpNode); + if (tmpNode === node) break; + } + if (component.length > 0) { + allComponents.push(component); + } + } + }; + for (const node of nodes) { + if (!indices[node.id] && indices[node.id] !== 0) { + getComponent(node); + } + } + return allComponents; +}; + +export function getConnectedComponents(graph: Graph, directed?: boolean): INode[][] { + if (directed) return detectStrongConnectComponents(graph); + return detectConnectedComponents(graph); +} diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index 6413f1b..816e8b7 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -9,4 +9,5 @@ export * from './dfs'; export * from './cosine-similarity'; export * from './nodes-cosine-similarity'; export * from './gaddi'; -export * from './mst'; \ No newline at end of file +export * from './connected-component'; +export * from './mst'; diff --git a/packages/graph/src/utils.ts b/packages/graph/src/utils.ts index 4a884fb..e383a1f 100644 --- a/packages/graph/src/utils.ts +++ b/packages/graph/src/utils.ts @@ -77,4 +77,3 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol }); return oneHotCode; }; - From f6a34cd0f126387ae1a8353912fce4f5ab43cede Mon Sep 17 00:00:00 2001 From: Cee Date: Sun, 8 Oct 2023 10:04:32 +0800 Subject: [PATCH 10/15] feat: v5 algorithm detect cycle (#74) * feat: add direction flag and visitOnce flag to dfs * feat: v5 algorithm detect cycle * test: unit test of detect cycle algorithm --- __tests__/data/detect-cycle-test-data.json | 1 + __tests__/unit/detect-cycle.spec.ts | 519 +++++++++++++++++++++ __tests__/unit/dfs.spec.ts | 2 - __tests__/utils/data.ts | 2 +- package.json | 2 +- packages/graph/src/detect-cycle.ts | 307 ++++++++++++ packages/graph/src/dfs.ts | 60 ++- packages/graph/src/index.ts | 1 + 8 files changed, 880 insertions(+), 14 deletions(-) create mode 100644 __tests__/data/detect-cycle-test-data.json create mode 100644 __tests__/unit/detect-cycle.spec.ts create mode 100644 packages/graph/src/detect-cycle.ts diff --git a/__tests__/data/detect-cycle-test-data.json b/__tests__/data/detect-cycle-test-data.json new file mode 100644 index 0000000..505713f --- /dev/null +++ b/__tests__/data/detect-cycle-test-data.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/__tests__/unit/detect-cycle.spec.ts b/__tests__/unit/detect-cycle.spec.ts new file mode 100644 index 0000000..db1cb92 --- /dev/null +++ b/__tests__/unit/detect-cycle.spec.ts @@ -0,0 +1,519 @@ +import { detectAllCycles, detectDirectedCycle } from '../../packages/graph/src' +import { dataTransformer } from '../utils/data'; +import { Graph } from "@antv/graphlib"; +import detectCycleTestData from '../data/detect-cycle-test-data.json'; + +const data = dataTransformer({ + nodes: [ + { + id: 'A', + }, + { + id: 'B', + }, + { + id: 'C', + }, + { + id: 'D', + }, + { + id: 'E', + }, + { + id: 'F', + }, + { + id: 'G', + }, + ], + edges: [ + { + source: 'A', + target: 'B', + }, + { + source: 'B', + target: 'C', + }, + { + source: 'A', + target: 'C', + }, + { + source: 'D', + target: 'A', + }, + { + source: 'D', + target: 'E', + }, + { + source: 'E', + target: 'F', + }, + ], +}); + +const graph = new Graph(data); +describe('detectDirectedCycle', () => { + it('should detect directed cycle', () => { + let result = detectDirectedCycle(graph); + expect(result).toBeNull(); + const cycleEdge = { + id: 'edge-6', + source: 'F', + target: 'D', + data: {} + } + graph.addEdge(cycleEdge); + result = detectDirectedCycle(graph); + expect(result).toEqual({ + D: 'F', + F: 'E', + E: 'D', + }); + graph.removeEdge('edge-6'); + }); + + it('detect all cycles in directed graph', () => { + graph.addEdge({ + id: 'edge-6', + data: {}, + source: 'C', + target: 'D', + }); + + const result = detectAllCycles(graph, true); + expect(result.length).toEqual(2); + const result2 = detectAllCycles(graph, true, ['B']); + expect(result2.length).toEqual(1); + expect(result2[0]).toEqual({ + A: { id: 'B', data: {} }, + B: { id: 'C', data: {} }, + C: { id: 'D', data: {} }, + D: { id: 'A', data: {} }, + }); + graph.removeEdge('edge-6'); + }); + + it('detect cycle in undirected graph', () => { + const result = detectAllCycles(graph); + expect(result.length).toEqual(1); + graph.addEdge({ + id: 'edge-6', + data: {}, + source: 'C', + target: 'D', + }); + const result2 = detectAllCycles(graph, false, ['B'], false); + expect(Object.keys(result2[0]).sort()).toEqual(['A', 'C', 'D']); + }); + + it('test another graph', () => { + const graphData = { + nodes: [ + { + id: '0', + label: '0', + }, + { + id: '1', + label: '1', + }, + { + id: '2', + label: '2', + }, + { + id: '3', + label: '3', + }, + { + id: '4', + label: '4', + }, + { + id: '5', + label: '5', + }, + { + id: '6', + label: '6', + }, + { + id: '7', + label: '7', + }, + { + id: '8', + label: '8', + }, + { + id: '9', + label: '9', + }, + { + id: '10', + label: '10', + }, + { + id: '11', + label: '11', + }, + { + id: '12', + label: '12', + }, + { + id: '13', + label: '13', + }, + { + id: '14', + label: '14', + }, + { + id: '15', + label: '15', + }, + { + id: '16', + label: '16', + }, + { + id: '17', + label: '17', + }, + { + id: '18', + label: '18', + }, + { + id: '19', + label: '19', + }, + { + id: '20', + label: '20', + }, + { + id: '21', + label: '21', + }, + { + id: '22', + label: '22', + }, + { + id: '23', + label: '23', + }, + { + id: '24', + label: '24', + }, + { + id: '25', + label: '25', + }, + { + id: '26', + label: '26', + }, + { + id: '27', + label: '27', + }, + { + id: '28', + label: '28', + }, + { + id: '29', + label: '29', + }, + { + id: '30', + label: '30', + }, + { + id: '31', + label: '31', + }, + { + id: '32', + label: '32', + }, + { + id: '33', + label: '33', + }, + ], + edges: [ + { + source: '0', + target: '1', + }, + { + source: '0', + target: '2', + }, + { + source: '3', + target: '0', + }, + { + source: '0', + target: '4', + }, + { + source: '5', + target: '0', + }, + { + source: '0', + target: '7', + }, + { + source: '0', + target: '8', + }, + { + source: '0', + target: '9', + }, + { + source: '0', + target: '10', + }, + { + source: '0', + target: '11', + }, + { + source: '0', + target: '13', + }, + { + source: '14', + target: '0', + }, + { + source: '0', + target: '15', + }, + { + source: '0', + target: '16', + }, + { + source: '2', + target: '3', + }, + { + source: '4', + target: '5', + }, + { + source: '4', + target: '6', + }, + { + source: '5', + target: '6', + }, + { + source: '7', + target: '13', + }, + { + source: '8', + target: '14', + }, + { + source: '9', + target: '10', + }, + { + source: '10', + target: '22', + }, + { + source: '10', + target: '14', + }, + { + source: '10', + target: '12', + }, + { + source: '10', + target: '24', + }, + { + source: '10', + target: '21', + }, + { + source: '10', + target: '20', + }, + { + source: '11', + target: '24', + }, + { + source: '11', + target: '22', + }, + { + source: '11', + target: '14', + }, + { + source: '12', + target: '13', + }, + { + source: '16', + target: '17', + }, + { + source: '16', + target: '18', + }, + { + source: '16', + target: '21', + }, + { + source: '16', + target: '22', + }, + { + source: '17', + target: '18', + }, + { + source: '17', + target: '20', + }, + { + source: '18', + target: '19', + }, + { + source: '19', + target: '20', + }, + { + source: '19', + target: '33', + }, + { + source: '19', + target: '22', + }, + { + source: '19', + target: '23', + }, + { + source: '20', + target: '21', + }, + { + source: '21', + target: '22', + }, + { + source: '22', + target: '24', + }, + { + source: '22', + target: '25', + }, + { + source: '22', + target: '26', + }, + { + source: '22', + target: '23', + }, + { + source: '22', + target: '28', + }, + { + source: '22', + target: '30', + }, + { + source: '22', + target: '31', + }, + { + source: '22', + target: '32', + }, + { + source: '22', + target: '33', + }, + { + source: '23', + target: '28', + }, + { + source: '23', + target: '27', + }, + { + source: '23', + target: '29', + }, + { + source: '23', + target: '30', + }, + { + source: '23', + target: '31', + }, + { + source: '23', + target: '33', + }, + { + source: '32', + target: '33', + }, + ], + }; + const graph = new Graph(dataTransformer(graphData)); + const result = detectAllCycles(graph, true, ['14']); + const result2 = detectAllCycles(graph); + expect(result.length).toEqual(4); + expect(result2.length).toEqual(27); + }); + it('test a large graph', () => { + detectCycleTestData.nodes.forEach((node: any) => { + node.label = node.olabel; + node.degree = 0; + data.edges.forEach((edge) => { + if (edge.source === node.id || edge.target === node.id) { + node.degree++; + } + }); + const graph = new Graph(dataTransformer(detectCycleTestData)); + + const directedCycles = detectAllCycles(graph, true); + expect(directedCycles.length).toEqual(0); + const undirectedCycles = detectAllCycles(graph, false, ['1084'], false); + expect(undirectedCycles.length).toEqual(1548); + }); + }); +}); diff --git a/__tests__/unit/dfs.spec.ts b/__tests__/unit/dfs.spec.ts index fb1e5d7..01b9689 100644 --- a/__tests__/unit/dfs.spec.ts +++ b/__tests__/unit/dfs.spec.ts @@ -87,10 +87,8 @@ const data = { const graph = new Graph(data); describe('depthFirstSearch', () => { it('should perform DFS operation on graph', () => { - const enterNodeCallback = jest.fn(); const leaveNodeCallback = jest.fn(); - // Traverse graphs without callbacks first to check default ones. depthFirstSearch(graph, 'A'); diff --git a/__tests__/utils/data.ts b/__tests__/utils/data.ts index 9c131d7..246c69a 100644 --- a/__tests__/utils/data.ts +++ b/__tests__/utils/data.ts @@ -9,7 +9,7 @@ export const dataTransformer = (data: { nodes: { id: NodeID, [key: string]: any return { nodes: nodes.map((n) => { const { id, ...rest } = n; - return { id, data: rest }; + return { id, data: rest ? rest : {} }; }), edges: edges.map((e, i) => { const { id, source, target, ...rest } = e; diff --git a/package.json b/package.json index e8f2ba9..0c4fcca 100644 --- a/package.json +++ b/package.json @@ -21,7 +21,7 @@ "build:ci": "pnpm -r run build:ci", "prepare": "husky install", "test": "jest", - "test_one": "jest ./__tests__/unit/mst.spec.ts", + "test_one": "jest ./__tests__/unit/detect-cycle.spec.ts", "coverage": "jest --coverage", "build:site": "vite build", "deploy": "gh-pages -d site/dist", diff --git a/packages/graph/src/detect-cycle.ts b/packages/graph/src/detect-cycle.ts new file mode 100644 index 0000000..0af03f8 --- /dev/null +++ b/packages/graph/src/detect-cycle.ts @@ -0,0 +1,307 @@ +import { depthFirstSearch } from './dfs'; +import { getConnectedComponents, detectStrongConnectComponents } from './connected-component'; +import { Graph, IAlgorithmCallbacks, INode, NodeData, NodeID } from './types'; +import { Node } from '@antv/graphlib'; + +/** + * Detects a directed cycle in a graph. + * + * @param graph The graph to detect the directed cycle in. + * @returns An object representing the detected directed cycle, where each key-value pair represents a node ID and its parent node ID in the cycle. + */ +export const detectDirectedCycle = (graph: Graph): { + [key: NodeID]: NodeID; +} => { + let cycle: { + [key: NodeID]: NodeID; + } = null; + const nodes = graph.getAllNodes(); + const dfsParentMap: { [key: NodeID]: NodeID } = {}; + // The set of all nodes that are not being accessed + const unvisitedSet: { [key: NodeID]: Node } = {}; + // The set of nodes being accessed + const visitingSet: { [key: NodeID]: NodeID } = {}; + // The set of all nodes that have been accessed + const visitedSet: { [key: NodeID]: NodeID } = {}; + // init unvisitedSet + nodes.forEach((node) => { + unvisitedSet[node.id] = node; + }); + const callbacks: IAlgorithmCallbacks = { + enter: ({ current: currentNodeId, previous: previousNodeId }) => { + if (visitingSet[currentNodeId]) { + // 如果当前节点正在访问中，则说明检测到环路了 + cycle = {}; + let currentCycleNodeId = currentNodeId; + let previousCycleNodeId = previousNodeId; + while (previousCycleNodeId !== currentNodeId) { + cycle[currentCycleNodeId] = previousCycleNodeId; + currentCycleNodeId = previousCycleNodeId; + previousCycleNodeId = dfsParentMap[previousCycleNodeId]; + } + cycle[currentCycleNodeId] = previousCycleNodeId; + } else { + visitingSet[currentNodeId] = currentNodeId; + delete unvisitedSet[currentNodeId]; + dfsParentMap[currentNodeId] = previousNodeId; + } + }, + leave: ({ current: currentNodeId }) => { + visitedSet[currentNodeId] = currentNodeId; + delete visitingSet[currentNodeId]; + }, + allowTraversal: () => { + if (cycle) { + return false; + } + return true; + }, + }; + for (let key of Object.keys(unvisitedSet)) { + depthFirstSearch(graph, key, callbacks, true, false); + } + return cycle; +}; + +/** + * Detects all undirected cycles in a graph. + * @param graph The graph to detect cycles in. + * @param nodeIds Optional array of node IDs to filter cycles by. + * @param include Specifies whether the filtered cycles should be included (true) or excluded (false). + * @returns An array of objects representing the detected cycles in the graph. + */ +export const detectAllUndirectedCycle = (graph: Graph, nodeIds?: NodeID[], include = true) => { + const allCycles: { [key: NodeID]: INode }[] = []; + const components = getConnectedComponents(graph, false); + // loop through all connected components + for (const component of components) { + if (!component.length) continue; + const root = component[0]; + const rootId = root.id; + const stack = [root]; + const parent = { [rootId]: root }; + const used = { [rootId]: new Set() }; + // walk a spanning tree to find cycles + while (stack.length > 0) { + const curNode = stack.pop(); + const curNodeId = curNode.id; + const neighbors = graph.getNeighbors(curNodeId); + // const neighbors = getNeighbors(curNodeId, graphData.edges); + for (let i = 0; i < neighbors.length; i += 1) { + const neighborId = neighbors[i].id; + const neighbor = graph.getAllNodes().find(node => node.id === neighborId); + if (neighborId === curNodeId) { + allCycles.push({ [neighborId]: curNode }); + } else if (!(neighborId in used)) { + // visit a new node + parent[neighborId] = curNode; + stack.push(neighbor); + used[neighborId] = new Set([curNode]); + } else if (!used[curNodeId].has(neighbor)) { + // a cycle found + let cycleValid = true; + const cyclePath = [neighbor, curNode]; + let p = parent[curNodeId]; + while (used[neighborId].size && !used[neighborId].has(p)) { + cyclePath.push(p); + if (p === parent[p.id]) break; + else p = parent[p.id]; + } + cyclePath.push(p); + if (nodeIds && include) { + cycleValid = false; + if (cyclePath.findIndex((node) => nodeIds.indexOf(node.id) > -1) > -1) { + cycleValid = true; + } + } else if (nodeIds && !include) { + if (cyclePath.findIndex((node) => nodeIds.indexOf(node.id) > -1) > -1) { + cycleValid = false; + } + } + // Format node list to cycle + if (cycleValid) { + const cycle: { [key: NodeID]: INode } = {}; + for (let index = 1; index < cyclePath.length; index += 1) { + cycle[cyclePath[index - 1].id] = cyclePath[index]; + } + if (cyclePath.length) { + cycle[cyclePath[cyclePath.length - 1].id] = cyclePath[0]; + } + allCycles.push(cycle); + } + used[neighborId].add(curNode); + } + } + } + } + return allCycles; +}; + +/** + * Detects all directed cycles in a graph. + * @param graph The graph to detect cycles in. + * @param nodeIds Optional array of node IDs to filter cycles by. + * @param include Specifies whether the filtered cycles should be included (true) or excluded (false). + * @returns An array of objects representing the detected cycles in the graph. + */ +export const detectAllDirectedCycle = (graph: Graph, nodeIds?: NodeID[], include = true) => { + const path: INode[] = []; // stack of nodes in current pate + const blocked = new Set(); + const B: { [key: NodeID]: Set } = {}; // remember portions of the graph that yield no elementary circuit + const allCycles: { [key: NodeID]: INode }[] = []; + const idx2Node: { + [key: number]: INode; + } = {}; + const node2Idx: { [key: NodeID]: number } = {}; + // unblock all blocked nodes + const unblock = (thisNode: INode) => { + const stack = [thisNode]; + while (stack.length > 0) { + const node = stack.pop(); + if (blocked.has(node)) { + blocked.delete(node); + B[node.id].forEach((n) => { + stack.push(n); + }); + B[node.id].clear(); + } + } + }; + + const circuit = (node: INode, start: INode, adjList: { [key: NodeID]: number[] }) => { + let closed = false; // whether a path is closed + if (nodeIds && include === false && nodeIds.indexOf(node.id) > -1) return closed; + path.push(node); + blocked.add(node); + const neighbors = adjList[node.id]; + for (let i = 0; i < neighbors.length; i += 1) { + const neighbor = idx2Node[neighbors[i]]; + if (neighbor === start) { + const cycle: { [key: NodeID]: INode } = {}; + for (let index = 1; index < path.length; index += 1) { + cycle[path[index - 1].id] = path[index]; + } + if (path.length) { + cycle[path[path.length - 1].id] = path[0]; + } + allCycles.push(cycle); + closed = true; + } else if (!blocked.has(neighbor)) { + if (circuit(neighbor, start, adjList)) { + closed = true; + } + } + } + if (closed) { + unblock(node); + } else { + for (let i = 0; i < neighbors.length; i += 1) { + const neighbor = idx2Node[neighbors[i]]; + if (!B[neighbor.id].has(node)) { + B[neighbor.id].add(node); + } + } + } + path.pop(); + return closed; + }; + + const nodes = graph.getAllNodes(); + + // Johnson's algorithm, sort nodes + for (let i = 0; i < nodes.length; i += 1) { + const node = nodes[i]; + const nodeId = node.id; + node2Idx[nodeId] = i; + idx2Node[i] = node; + } + // If there are specified included nodes, the specified nodes are sorted first in order to end the search early + if (nodeIds && include) { + for (let i = 0; i < nodeIds.length; i++) { + const nodeId = nodeIds[i]; + node2Idx[nodes[i].id] = node2Idx[nodeId]; + node2Idx[nodeId] = 0; + idx2Node[0] = nodes.find(node => node.id === nodeId); + idx2Node[node2Idx[nodes[i].id]] = nodes[i]; + } + } + + // Returns the adjList of the strongly connected component of the node (order > = nodeOrder) + const getMinComponentAdj = (components: INode[][]) => { + let minCompIdx; + let minIdx = Infinity; + // Find least component and the lowest node + for (let i = 0; i < components.length; i += 1) { + const comp = components[i]; + for (let j = 0; j < comp.length; j++) { + const nodeIdx = node2Idx[comp[j].id]; + if (nodeIdx < minIdx) { + minIdx = nodeIdx; + minCompIdx = i; + } + } + } + const component = components[minCompIdx]; + const adjList: { [key: NodeID]: number[] } = {}; + for (let i = 0; i < component.length; i += 1) { + const node = component[i]; + adjList[node.id] = []; + for (const neighbor of graph.getRelatedEdges(node.id, "out").map(n => n.target).filter((n) => component.map(c => c.id).indexOf(n) > -1)) { + // 对自环情况 (点连向自身) 特殊处理：记录自环，但不加入adjList + if (neighbor === node.id && !(include === false && nodeIds.indexOf(node.id) > -1)) { + allCycles.push({ [node.id]: node }); + } else { + adjList[node.id].push(node2Idx[neighbor]); + } + } + } + return { + component, + adjList, + minIdx, + }; + }; + + let nodeIdx = 0; + while (nodeIdx < nodes.length) { + const sccs = detectStrongConnectComponents(graph).filter( + (component) => component.length > 1, + ); + if (sccs.length === 0) break; + const scc = getMinComponentAdj(sccs); + const { minIdx, adjList, component } = scc; + if (component.length > 1) { + component.forEach((node) => { + B[node.id] = new Set(); + }); + const startNode = idx2Node[minIdx]; + // StartNode is not in the specified node to include. End the search ahead of time. + if (nodeIds && include && nodeIds.indexOf(startNode.id) === -1) return allCycles; + circuit(startNode, startNode, adjList); + nodeIdx = minIdx + 1; + } else { + break; + } + break; + } + return allCycles; +}; + +/** + * Detects all cycles in a graph. + * @param graph The graph to detect cycles in. + * @param directed Specifies whether the graph is directed (true) or undirected (false). + * @param nodeIds Optional array of node IDs to filter cycles by. + * @param include Specifies whether the filtered cycles should be included (true) or excluded (false). + * @returns An array of objects representing the detected cycles in the graph. + */ +export const detectAllCycles = ( + graph: Graph, + directed?: boolean, + nodeIds?: string[], + include = true, +) => { + if (directed) return detectAllDirectedCycle(graph, nodeIds, include); + return detectAllUndirectedCycle(graph, nodeIds, include); +}; + diff --git a/packages/graph/src/dfs.ts b/packages/graph/src/dfs.ts index f66be8a..723f44c 100644 --- a/packages/graph/src/dfs.ts +++ b/packages/graph/src/dfs.ts @@ -1,5 +1,10 @@ import { Graph, IAlgorithmCallbacks, NodeID } from './types'; +/** + * Initializes the callback functions for the depth-first search algorithm. + * @param callbacks (Optional) The original callbacks object containing custom callback functions. + * @returns The initialized callbacks object. + */ function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks) { const initiatedCallback = callbacks; const stubCallback = () => { }; @@ -7,32 +12,57 @@ function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallback initiatedCallback.allowTraversal = callbacks.allowTraversal || allowTraversalCallback; initiatedCallback.enter = callbacks.enter || stubCallback; initiatedCallback.leave = callbacks.leave || stubCallback; - return initiatedCallback; } +/** + * Recursively performs a depth-first search on a graph starting from a specified node. + * @param graph The graph to perform the depth-first search on. + * @param currentNodeId The ID of the current node being visited. + * @param previousNodeId The ID of the previous node visited. + * @param callbacks The callback functions for the depth-first search algorithm. + * @param visit A set containing the visited node IDs. + * @param directed A boolean indicating whether the graph is directed. + * @param visitOnce A boolean indicating whether each node should be visited only once. + */ function depthFirstSearchRecursive( graph: Graph, currentNodeId: NodeID, previousNodeId: NodeID, callbacks: IAlgorithmCallbacks, - visit: Set + visit: Set, + directed: boolean, + visitOnce: boolean, ) { callbacks.enter({ current: currentNodeId, previous: previousNodeId, }); - graph.getNeighbors(currentNodeId).forEach((nextNode) => { + const neighbors = directed + ? + graph.getRelatedEdges(currentNodeId, "out").map(e => graph.getNode(e.target)) + : + graph.getNeighbors(currentNodeId) + ; + neighbors.forEach((nextNode) => { const nextNodeId = nextNode.id; + // `Visit` is not considered when judging recursive conditions if ( - callbacks.allowTraversal({ - previous: previousNodeId, - current: currentNodeId, - next: nextNodeId, - }) && !visit.has(nextNodeId) + visitOnce ? + (callbacks.allowTraversal({ + previous: previousNodeId, + current: currentNodeId, + next: nextNodeId, + }) && !visit.has(nextNodeId)) + : + callbacks.allowTraversal({ + previous: previousNodeId, + current: currentNodeId, + next: nextNodeId, + }) ) { visit.add(nextNodeId); - depthFirstSearchRecursive(graph, nextNodeId, currentNodeId, callbacks, visit); + depthFirstSearchRecursive(graph, nextNodeId, currentNodeId, callbacks, visit, directed, visitOnce); } }); callbacks.leave({ @@ -41,12 +71,22 @@ function depthFirstSearchRecursive( }); } +/** + * Performs a depth-first search on a graph starting from a specified node. + * @param graph The graph to perform the depth-first search on. + * @param startNodeId The ID of the node to start the search from. + * @param originalCallbacks (Optional) The original callbacks object containing custom callback functions. + * @param directed A boolean indicating whether the graph is directed (default: false). + * @param visitOnce A boolean indicating whether each node should be visited only once (default: true). + */ export function depthFirstSearch( graph: Graph, startNodeId: NodeID, originalCallbacks?: IAlgorithmCallbacks, + directed: boolean = false, + visitOnce: boolean = true ) { const visit = new Set(); visit.add(startNodeId); - depthFirstSearchRecursive(graph, startNodeId, '', initCallbacks(originalCallbacks), visit); + depthFirstSearchRecursive(graph, startNodeId, '', initCallbacks(originalCallbacks), visit, directed, visitOnce); } diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index 816e8b7..ae8dce6 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -11,3 +11,4 @@ export * from './nodes-cosine-similarity'; export * from './gaddi'; export * from './connected-component'; export * from './mst'; +export * from './detect-cycle'; \ No newline at end of file From 305a20afce72fbd817e30cfc5abcc2b456956730 Mon Sep 17 00:00:00 2001 From: Yanyan Wang Date: Mon, 16 Oct 2023 11:16:53 +0800 Subject: [PATCH 11/15] fix: louvain data pollution (#76) * fix: louvain data pollution * chore: refine --- __tests__/unit/louvain.spec.ts | 80 +++- __tests__/utils/data.ts | 30 +- packages/graph/src/bfs.ts | 27 +- packages/graph/src/connected-component.ts | 18 +- packages/graph/src/detect-cycle.ts | 534 ++++++++++++---------- packages/graph/src/dfs.ts | 76 +-- packages/graph/src/louvain.ts | 231 ++++++---- packages/graph/src/types.ts | 21 +- 8 files changed, 587 insertions(+), 430 deletions(-) diff --git a/__tests__/unit/louvain.spec.ts b/__tests__/unit/louvain.spec.ts index 9f9185f..639db91 100644 --- a/__tests__/unit/louvain.spec.ts +++ b/__tests__/unit/louvain.spec.ts @@ -1,38 +1,68 @@ -import { Graph } from "@antv/graphlib"; -import { louvain, iLouvain } from "../../packages/graph/src"; -import * as propertiesGraphData from "../data/cluster-origin-properties-data.json"; +import { Graph } from '@antv/graphlib'; +import { louvain, iLouvain } from '../../packages/graph/src'; +import * as propertiesGraphData from '../data/cluster-origin-properties-data.json'; describe('Louvain', () => { it('simple louvain', () => { const graph = new Graph({ nodes: [ - { id: '0', data: {} }, { id: '1', data: {} }, { id: '2', data: {} }, { id: '3', data: {} }, { id: '4', data: {} }, - { id: '5', data: {} }, { id: '6', data: {} }, { id: '7', data: {} }, { id: '8', data: {} }, { id: '9', data: {} }, - { id: '10', data: {} }, { id: '11', data: {} }, { id: '12', data: {} }, { id: '13', data: {} }, { id: '14', data: {} }, + { id: '0', data: {} }, + { id: '1', data: {} }, + { id: '2', data: {} }, + { id: '3', data: {} }, + { id: '4', data: {} }, + { id: '5', data: {} }, + { id: '6', data: {} }, + { id: '7', data: {} }, + { id: '8', data: {} }, + { id: '9', data: {} }, + { id: '10', data: {} }, + { id: '11', data: {} }, + { id: '12', data: {} }, + { id: '13', data: {} }, + { id: '14', data: {} }, ], edges: [ - { id: 'e1', source: '0', target: '1', data: {} }, { id: 'e2', source: '0', target: '2', data: {} }, { id: 'e3', source: '0', target: '3', data: {} }, { id: 'e4', source: '0', target: '4', data: {} }, - { id: 'e5', source: '1', target: '2', data: {} }, { id: 'e6', source: '1', target: '3', data: {} }, { id: 'e7', source: '1', target: '4', data: {} }, - { id: 'e8', source: '2', target: '3', data: {} }, { id: 'e9', source: '2', target: '4', data: {} }, + { id: 'e1', source: '0', target: '1', data: {} }, + { id: 'e2', source: '0', target: '2', data: {} }, + { id: 'e3', source: '0', target: '3', data: {} }, + { id: 'e4', source: '0', target: '4', data: {} }, + { id: 'e5', source: '1', target: '2', data: {} }, + { id: 'e6', source: '1', target: '3', data: {} }, + { id: 'e7', source: '1', target: '4', data: {} }, + { id: 'e8', source: '2', target: '3', data: {} }, + { id: 'e9', source: '2', target: '4', data: {} }, { id: 'e10', source: '3', target: '4', data: {} }, { id: 'e11', source: '0', target: '0', data: {} }, { id: 'e12', source: '0', target: '0', data: {} }, { id: 'e13', source: '0', target: '0', data: {} }, - - { id: 'e14', source: '5', target: '6', data: {weight: 5} }, { id: 'e15', source: '5', target: '7', data: {} }, { id: 'e16', source: '5', target: '8', data: {} }, { id: 'e17', source: '5', target: '9', data: {} }, - { id: 'e18', source: '6', target: '7', data: {} }, { id: 'e19', source: '6', target: '8', data: {} }, { id: 'e20', source: '6', target: '9', data: {} }, - { id: 'e21', source: '7', target: '8', data: {} }, { id: 'e22', source: '7', target: '9', data: {} }, - { id: 'e23',source: '8', target: '9', data: {} }, - - { id: 'e24',source: '10', target: '11', data: {} }, { id: 'e25',source: '10', target: '12', data: {} }, { id: 'e26',source: '10', target: '13', data: {} }, { id: 'e27',source: '10', target: '14', data: {} }, - { id: 'e28',source: '11', target: '12', data: {} }, { id: 'e29',source: '11', target: '13', data: {} }, { id: 'e30',source: '11', target: '14', data: {} }, - { id: 'e31',source: '12', target: '13', data: {} }, { id: 'e32',source: '12', target: '14', data: {} }, - { id: 'e33',source: '13', target: '14', data: { weight: 5 } }, - - { id: 'e34',source: '0', target: '5', data: {}}, - { id: 'e35',source: '5', target: '10', data: {} }, - { id: 'e36',source: '10', target: '0', data: {} }, - { id: 'e37',source: '10', target: '0', data: {} }, + + { id: 'e14', source: '5', target: '6', data: { weight: 5 } }, + { id: 'e15', source: '5', target: '7', data: {} }, + { id: 'e16', source: '5', target: '8', data: {} }, + { id: 'e17', source: '5', target: '9', data: {} }, + { id: 'e18', source: '6', target: '7', data: {} }, + { id: 'e19', source: '6', target: '8', data: {} }, + { id: 'e20', source: '6', target: '9', data: {} }, + { id: 'e21', source: '7', target: '8', data: {} }, + { id: 'e22', source: '7', target: '9', data: {} }, + { id: 'e23', source: '8', target: '9', data: {} }, + + { id: 'e24', source: '10', target: '11', data: {} }, + { id: 'e25', source: '10', target: '12', data: {} }, + { id: 'e26', source: '10', target: '13', data: {} }, + { id: 'e27', source: '10', target: '14', data: {} }, + { id: 'e28', source: '11', target: '12', data: {} }, + { id: 'e29', source: '11', target: '13', data: {} }, + { id: 'e30', source: '11', target: '14', data: {} }, + { id: 'e31', source: '12', target: '13', data: {} }, + { id: 'e32', source: '12', target: '14', data: {} }, + { id: 'e33', source: '13', target: '14', data: { weight: 5 } }, + + { id: 'e34', source: '0', target: '5', data: {} }, + { id: 'e35', source: '5', target: '10', data: {} }, + { id: 'e36', source: '10', target: '0', data: {} }, + { id: 'e37', source: '10', target: '0', data: {} }, ], }); const clusteredData = louvain(graph, false, 'weight'); @@ -64,4 +94,4 @@ describe('Louvain', () => { expect(clusteredData.clusters[2].sumTot).toBe(4); expect(clusteredData.clusterEdges.length).toBe(7); }); -}); \ No newline at end of file +}); diff --git a/__tests__/utils/data.ts b/__tests__/utils/data.ts index 246c69a..5d33b89 100644 --- a/__tests__/utils/data.ts +++ b/__tests__/utils/data.ts @@ -1,19 +1,23 @@ -import { NodeID, INode, IEdge } from "../../packages/graph/src/types"; +import { ID } from '@antv/graphlib'; +import { INode, IEdge } from '../../packages/graph/src/types'; /** * Convert the old version of the data format to the new version * @param data old data * @return {{nodes:INode[],edges:IEdge[]}} new data */ -export const dataTransformer = (data: { nodes: { id: NodeID, [key: string]: any }[], edges: { source: NodeID, target: NodeID, [key: string]: any }[] }): { nodes: INode[], edges: IEdge[] } => { - const { nodes, edges } = data; - return { - nodes: nodes.map((n) => { - const { id, ...rest } = n; - return { id, data: rest ? rest : {} }; - }), - edges: edges.map((e, i) => { - const { id, source, target, ...rest } = e; - return { id: id ? id : `edge-${i}`, target, source, data: rest }; - }), - }; +export const dataTransformer = (data: { + nodes: { id: ID; [key: string]: any }[]; + edges: { source: ID; target: ID; [key: string]: any }[]; +}): { nodes: INode[]; edges: IEdge[] } => { + const { nodes, edges } = data; + return { + nodes: nodes.map((n) => { + const { id, ...rest } = n; + return { id, data: rest ? rest : {} }; + }), + edges: edges.map((e, i) => { + const { id, source, target, ...rest } = e; + return { id: id ? id : `edge-${i}`, target, source, data: rest }; + }), + }; }; diff --git a/packages/graph/src/bfs.ts b/packages/graph/src/bfs.ts index 209b8e1..94e8931 100644 --- a/packages/graph/src/bfs.ts +++ b/packages/graph/src/bfs.ts @@ -1,5 +1,6 @@ +import { ID } from '@antv/graphlib'; import Queue from './structs/queue'; -import { Graph, IAlgorithmCallbacks, NodeID } from './types'; +import { Graph, IAlgorithmCallbacks } from './types'; /** * @param startNodeId The ID of the bfs traverse starting node. @@ -8,11 +9,14 @@ import { Graph, IAlgorithmCallbacks, NodeID } from './types'; - enterNode: Called when BFS visits a node. - leaveNode: Called after BFS visits the node. */ -function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks) { +function initCallbacks( + callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks +) { const initiatedCallback = callbacks; - const stubCallback = () => { }; + const stubCallback = () => {}; const allowTraversalCallback = () => true; - initiatedCallback.allowTraversal = callbacks.allowTraversal || allowTraversalCallback; + initiatedCallback.allowTraversal = + callbacks.allowTraversal || allowTraversalCallback; initiatedCallback.enter = callbacks.enter || stubCallback; initiatedCallback.leave = callbacks.leave || stubCallback; return initiatedCallback; @@ -26,19 +30,19 @@ Performs breadth-first search (BFS) traversal on a graph. */ export const breadthFirstSearch = ( graph: Graph, - startNodeId: NodeID, - originalCallbacks?: IAlgorithmCallbacks, + startNodeId: ID, + originalCallbacks?: IAlgorithmCallbacks ) => { - const visit = new Set(); + const visit = new Set(); const callbacks = initCallbacks(originalCallbacks); - const nodeQueue = new Queue(); + const nodeQueue = new Queue(); // init Queue. Enqueue node ID. nodeQueue.enqueue(startNodeId); visit.add(startNodeId); - let previousNodeId: NodeID = ''; + let previousNodeId: ID = ''; // 遍历队列中的所有顶点 while (!nodeQueue.isEmpty()) { - const currentNodeId: NodeID = nodeQueue.dequeue(); + const currentNodeId: ID = nodeQueue.dequeue(); callbacks.enter({ current: currentNodeId, previous: previousNodeId, @@ -52,7 +56,8 @@ export const breadthFirstSearch = ( previous: previousNodeId, current: currentNodeId, next: nextNodeId, - }) && !visit.has(nextNodeId) + }) && + !visit.has(nextNodeId) ) { visit.add(nextNodeId); nodeQueue.enqueue(nextNodeId); diff --git a/packages/graph/src/connected-component.ts b/packages/graph/src/connected-component.ts index ce00e0f..32476d4 100644 --- a/packages/graph/src/connected-component.ts +++ b/packages/graph/src/connected-component.ts @@ -1,4 +1,5 @@ -import { Graph, INode, NodeID } from './types'; +import { ID } from '@antv/graphlib'; +import { Graph, INode } from './types'; /** * Generate all connected components for an undirected graph * @param graph @@ -6,7 +7,7 @@ import { Graph, INode, NodeID } from './types'; export const detectConnectedComponents = (graph: Graph): INode[][] => { const nodes = graph.getAllNodes(); const allComponents: INode[][] = []; - const visited: { [key: NodeID]: boolean } = {}; + const visited: { [key: ID]: boolean } = {}; const nodeStack: INode[] = []; const getComponent = (node: INode) => { nodeStack.push(node); @@ -49,9 +50,9 @@ export const detectStrongConnectComponents = (graph: Graph): INode[][] => { const nodes = graph.getAllNodes(); const nodeStack: INode[] = []; // Assist to determine whether it is already in the stack to reduce the search overhead - const inStack: { [key: NodeID]: boolean } = {}; - const indices: { [key: NodeID]: number } = {}; - const lowLink: { [key: NodeID]: number } = {}; + const inStack: { [key: ID]: boolean } = {}; + const indices: { [key: ID]: number } = {}; + const lowLink: { [key: ID]: number } = {}; const allComponents: INode[][] = []; let index = 0; const getComponent = (node: INode) => { @@ -61,7 +62,7 @@ export const detectStrongConnectComponents = (graph: Graph): INode[][] => { index += 1; nodeStack.push(node); inStack[node.id] = true; - const relatedEdges = graph.getRelatedEdges(node.id, "out"); + const relatedEdges = graph.getRelatedEdges(node.id, 'out'); for (let i = 0; i < relatedEdges.length; i++) { const targetNodeID = relatedEdges[i].target; if (!indices[targetNodeID] && indices[targetNodeID] !== 0) { @@ -98,7 +99,10 @@ export const detectStrongConnectComponents = (graph: Graph): INode[][] => { return allComponents; }; -export function getConnectedComponents(graph: Graph, directed?: boolean): INode[][] { +export function getConnectedComponents( + graph: Graph, + directed?: boolean +): INode[][] { if (directed) return detectStrongConnectComponents(graph); return detectConnectedComponents(graph); } diff --git a/packages/graph/src/detect-cycle.ts b/packages/graph/src/detect-cycle.ts index 0af03f8..dbd3f59 100644 --- a/packages/graph/src/detect-cycle.ts +++ b/packages/graph/src/detect-cycle.ts @@ -1,7 +1,10 @@ +import { ID, Node } from '@antv/graphlib'; import { depthFirstSearch } from './dfs'; -import { getConnectedComponents, detectStrongConnectComponents } from './connected-component'; -import { Graph, IAlgorithmCallbacks, INode, NodeData, NodeID } from './types'; -import { Node } from '@antv/graphlib'; +import { + getConnectedComponents, + detectStrongConnectComponents, +} from './connected-component'; +import { Graph, IAlgorithmCallbacks, INode, NodeData } from './types'; /** * Detects a directed cycle in a graph. @@ -9,58 +12,60 @@ import { Node } from '@antv/graphlib'; * @param graph The graph to detect the directed cycle in. * @returns An object representing the detected directed cycle, where each key-value pair represents a node ID and its parent node ID in the cycle. */ -export const detectDirectedCycle = (graph: Graph): { - [key: NodeID]: NodeID; +export const detectDirectedCycle = ( + graph: Graph +): { + [key: ID]: ID; } => { - let cycle: { - [key: NodeID]: NodeID; - } = null; - const nodes = graph.getAllNodes(); - const dfsParentMap: { [key: NodeID]: NodeID } = {}; - // The set of all nodes that are not being accessed - const unvisitedSet: { [key: NodeID]: Node } = {}; - // The set of nodes being accessed - const visitingSet: { [key: NodeID]: NodeID } = {}; - // The set of all nodes that have been accessed - const visitedSet: { [key: NodeID]: NodeID } = {}; - // init unvisitedSet - nodes.forEach((node) => { - unvisitedSet[node.id] = node; - }); - const callbacks: IAlgorithmCallbacks = { - enter: ({ current: currentNodeId, previous: previousNodeId }) => { - if (visitingSet[currentNodeId]) { - // 如果当前节点正在访问中，则说明检测到环路了 - cycle = {}; - let currentCycleNodeId = currentNodeId; - let previousCycleNodeId = previousNodeId; - while (previousCycleNodeId !== currentNodeId) { - cycle[currentCycleNodeId] = previousCycleNodeId; - currentCycleNodeId = previousCycleNodeId; - previousCycleNodeId = dfsParentMap[previousCycleNodeId]; - } - cycle[currentCycleNodeId] = previousCycleNodeId; - } else { - visitingSet[currentNodeId] = currentNodeId; - delete unvisitedSet[currentNodeId]; - dfsParentMap[currentNodeId] = previousNodeId; - } - }, - leave: ({ current: currentNodeId }) => { - visitedSet[currentNodeId] = currentNodeId; - delete visitingSet[currentNodeId]; - }, - allowTraversal: () => { - if (cycle) { - return false; - } - return true; - }, - }; - for (let key of Object.keys(unvisitedSet)) { - depthFirstSearch(graph, key, callbacks, true, false); - } - return cycle; + let cycle: { + [key: ID]: ID; + } = null; + const nodes = graph.getAllNodes(); + const dfsParentMap: { [key: ID]: ID } = {}; + // The set of all nodes that are not being accessed + const unvisitedSet: { [key: ID]: Node } = {}; + // The set of nodes being accessed + const visitingSet: { [key: ID]: ID } = {}; + // The set of all nodes that have been accessed + const visitedSet: { [key: ID]: ID } = {}; + // init unvisitedSet + nodes.forEach((node) => { + unvisitedSet[node.id] = node; + }); + const callbacks: IAlgorithmCallbacks = { + enter: ({ current: currentNodeId, previous: previousNodeId }) => { + if (visitingSet[currentNodeId]) { + // 如果当前节点正在访问中，则说明检测到环路了 + cycle = {}; + let currentCycleNodeId = currentNodeId; + let previousCycleNodeId = previousNodeId; + while (previousCycleNodeId !== currentNodeId) { + cycle[currentCycleNodeId] = previousCycleNodeId; + currentCycleNodeId = previousCycleNodeId; + previousCycleNodeId = dfsParentMap[previousCycleNodeId]; + } + cycle[currentCycleNodeId] = previousCycleNodeId; + } else { + visitingSet[currentNodeId] = currentNodeId; + delete unvisitedSet[currentNodeId]; + dfsParentMap[currentNodeId] = previousNodeId; + } + }, + leave: ({ current: currentNodeId }) => { + visitedSet[currentNodeId] = currentNodeId; + delete visitingSet[currentNodeId]; + }, + allowTraversal: () => { + if (cycle) { + return false; + } + return true; + }, + }; + for (let key of Object.keys(unvisitedSet)) { + depthFirstSearch(graph, key, callbacks, true, false); + } + return cycle; }; /** @@ -70,71 +75,81 @@ export const detectDirectedCycle = (graph: Graph): { * @param include Specifies whether the filtered cycles should be included (true) or excluded (false). * @returns An array of objects representing the detected cycles in the graph. */ -export const detectAllUndirectedCycle = (graph: Graph, nodeIds?: NodeID[], include = true) => { - const allCycles: { [key: NodeID]: INode }[] = []; - const components = getConnectedComponents(graph, false); - // loop through all connected components - for (const component of components) { - if (!component.length) continue; - const root = component[0]; - const rootId = root.id; - const stack = [root]; - const parent = { [rootId]: root }; - const used = { [rootId]: new Set() }; - // walk a spanning tree to find cycles - while (stack.length > 0) { - const curNode = stack.pop(); - const curNodeId = curNode.id; - const neighbors = graph.getNeighbors(curNodeId); - // const neighbors = getNeighbors(curNodeId, graphData.edges); - for (let i = 0; i < neighbors.length; i += 1) { - const neighborId = neighbors[i].id; - const neighbor = graph.getAllNodes().find(node => node.id === neighborId); - if (neighborId === curNodeId) { - allCycles.push({ [neighborId]: curNode }); - } else if (!(neighborId in used)) { - // visit a new node - parent[neighborId] = curNode; - stack.push(neighbor); - used[neighborId] = new Set([curNode]); - } else if (!used[curNodeId].has(neighbor)) { - // a cycle found - let cycleValid = true; - const cyclePath = [neighbor, curNode]; - let p = parent[curNodeId]; - while (used[neighborId].size && !used[neighborId].has(p)) { - cyclePath.push(p); - if (p === parent[p.id]) break; - else p = parent[p.id]; - } - cyclePath.push(p); - if (nodeIds && include) { - cycleValid = false; - if (cyclePath.findIndex((node) => nodeIds.indexOf(node.id) > -1) > -1) { - cycleValid = true; - } - } else if (nodeIds && !include) { - if (cyclePath.findIndex((node) => nodeIds.indexOf(node.id) > -1) > -1) { - cycleValid = false; - } - } - // Format node list to cycle - if (cycleValid) { - const cycle: { [key: NodeID]: INode } = {}; - for (let index = 1; index < cyclePath.length; index += 1) { - cycle[cyclePath[index - 1].id] = cyclePath[index]; - } - if (cyclePath.length) { - cycle[cyclePath[cyclePath.length - 1].id] = cyclePath[0]; - } - allCycles.push(cycle); - } - used[neighborId].add(curNode); - } +export const detectAllUndirectedCycle = ( + graph: Graph, + nodeIds?: ID[], + include = true +) => { + const allCycles: { [key: ID]: INode }[] = []; + const components = getConnectedComponents(graph, false); + // loop through all connected components + for (const component of components) { + if (!component.length) continue; + const root = component[0]; + const rootId = root.id; + const stack = [root]; + const parent = { [rootId]: root }; + const used = { [rootId]: new Set() }; + // walk a spanning tree to find cycles + while (stack.length > 0) { + const curNode = stack.pop(); + const curNodeId = curNode.id; + const neighbors = graph.getNeighbors(curNodeId); + // const neighbors = getNeighbors(curNodeId, graphData.edges); + for (let i = 0; i < neighbors.length; i += 1) { + const neighborId = neighbors[i].id; + const neighbor = graph + .getAllNodes() + .find((node) => node.id === neighborId); + if (neighborId === curNodeId) { + allCycles.push({ [neighborId]: curNode }); + } else if (!(neighborId in used)) { + // visit a new node + parent[neighborId] = curNode; + stack.push(neighbor); + used[neighborId] = new Set([curNode]); + } else if (!used[curNodeId].has(neighbor)) { + // a cycle found + let cycleValid = true; + const cyclePath = [neighbor, curNode]; + let p = parent[curNodeId]; + while (used[neighborId].size && !used[neighborId].has(p)) { + cyclePath.push(p); + if (p === parent[p.id]) break; + else p = parent[p.id]; + } + cyclePath.push(p); + if (nodeIds && include) { + cycleValid = false; + if ( + cyclePath.findIndex((node) => nodeIds.indexOf(node.id) > -1) > -1 + ) { + cycleValid = true; + } + } else if (nodeIds && !include) { + if ( + cyclePath.findIndex((node) => nodeIds.indexOf(node.id) > -1) > -1 + ) { + cycleValid = false; + } + } + // Format node list to cycle + if (cycleValid) { + const cycle: { [key: ID]: INode } = {}; + for (let index = 1; index < cyclePath.length; index += 1) { + cycle[cyclePath[index - 1].id] = cyclePath[index]; } + if (cyclePath.length) { + cycle[cyclePath[cyclePath.length - 1].id] = cyclePath[0]; + } + allCycles.push(cycle); + } + used[neighborId].add(curNode); } + } } - return allCycles; + } + return allCycles; }; /** @@ -144,147 +159,163 @@ export const detectAllUndirectedCycle = (graph: Graph, nodeIds?: NodeID[], inclu * @param include Specifies whether the filtered cycles should be included (true) or excluded (false). * @returns An array of objects representing the detected cycles in the graph. */ -export const detectAllDirectedCycle = (graph: Graph, nodeIds?: NodeID[], include = true) => { - const path: INode[] = []; // stack of nodes in current pate - const blocked = new Set(); - const B: { [key: NodeID]: Set } = {}; // remember portions of the graph that yield no elementary circuit - const allCycles: { [key: NodeID]: INode }[] = []; - const idx2Node: { - [key: number]: INode; - } = {}; - const node2Idx: { [key: NodeID]: number } = {}; - // unblock all blocked nodes - const unblock = (thisNode: INode) => { - const stack = [thisNode]; - while (stack.length > 0) { - const node = stack.pop(); - if (blocked.has(node)) { - blocked.delete(node); - B[node.id].forEach((n) => { - stack.push(n); - }); - B[node.id].clear(); - } - } - }; +export const detectAllDirectedCycle = ( + graph: Graph, + nodeIds?: ID[], + include = true +) => { + const path: INode[] = []; // stack of nodes in current pate + const blocked = new Set(); + const B: { [key: ID]: Set } = {}; // remember portions of the graph that yield no elementary circuit + const allCycles: { [key: ID]: INode }[] = []; + const idx2Node: { + [key: number]: INode; + } = {}; + const node2Idx: { [key: ID]: number } = {}; + // unblock all blocked nodes + const unblock = (thisNode: INode) => { + const stack = [thisNode]; + while (stack.length > 0) { + const node = stack.pop(); + if (blocked.has(node)) { + blocked.delete(node); + B[node.id].forEach((n) => { + stack.push(n); + }); + B[node.id].clear(); + } + } + }; - const circuit = (node: INode, start: INode, adjList: { [key: NodeID]: number[] }) => { - let closed = false; // whether a path is closed - if (nodeIds && include === false && nodeIds.indexOf(node.id) > -1) return closed; - path.push(node); - blocked.add(node); - const neighbors = adjList[node.id]; - for (let i = 0; i < neighbors.length; i += 1) { - const neighbor = idx2Node[neighbors[i]]; - if (neighbor === start) { - const cycle: { [key: NodeID]: INode } = {}; - for (let index = 1; index < path.length; index += 1) { - cycle[path[index - 1].id] = path[index]; - } - if (path.length) { - cycle[path[path.length - 1].id] = path[0]; - } - allCycles.push(cycle); - closed = true; - } else if (!blocked.has(neighbor)) { - if (circuit(neighbor, start, adjList)) { - closed = true; - } - } + const circuit = ( + node: INode, + start: INode, + adjList: { [key: ID]: number[] } + ) => { + let closed = false; // whether a path is closed + if (nodeIds && include === false && nodeIds.indexOf(node.id) > -1) + return closed; + path.push(node); + blocked.add(node); + const neighbors = adjList[node.id]; + for (let i = 0; i < neighbors.length; i += 1) { + const neighbor = idx2Node[neighbors[i]]; + if (neighbor === start) { + const cycle: { [key: ID]: INode } = {}; + for (let index = 1; index < path.length; index += 1) { + cycle[path[index - 1].id] = path[index]; } - if (closed) { - unblock(node); - } else { - for (let i = 0; i < neighbors.length; i += 1) { - const neighbor = idx2Node[neighbors[i]]; - if (!B[neighbor.id].has(node)) { - B[neighbor.id].add(node); - } - } + if (path.length) { + cycle[path[path.length - 1].id] = path[0]; } - path.pop(); - return closed; - }; - - const nodes = graph.getAllNodes(); - - // Johnson's algorithm, sort nodes - for (let i = 0; i < nodes.length; i += 1) { - const node = nodes[i]; - const nodeId = node.id; - node2Idx[nodeId] = i; - idx2Node[i] = node; + allCycles.push(cycle); + closed = true; + } else if (!blocked.has(neighbor)) { + if (circuit(neighbor, start, adjList)) { + closed = true; + } + } } - // If there are specified included nodes, the specified nodes are sorted first in order to end the search early - if (nodeIds && include) { - for (let i = 0; i < nodeIds.length; i++) { - const nodeId = nodeIds[i]; - node2Idx[nodes[i].id] = node2Idx[nodeId]; - node2Idx[nodeId] = 0; - idx2Node[0] = nodes.find(node => node.id === nodeId); - idx2Node[node2Idx[nodes[i].id]] = nodes[i]; + if (closed) { + unblock(node); + } else { + for (let i = 0; i < neighbors.length; i += 1) { + const neighbor = idx2Node[neighbors[i]]; + if (!B[neighbor.id].has(node)) { + B[neighbor.id].add(node); } + } } + path.pop(); + return closed; + }; - // Returns the adjList of the strongly connected component of the node (order > = nodeOrder) - const getMinComponentAdj = (components: INode[][]) => { - let minCompIdx; - let minIdx = Infinity; - // Find least component and the lowest node - for (let i = 0; i < components.length; i += 1) { - const comp = components[i]; - for (let j = 0; j < comp.length; j++) { - const nodeIdx = node2Idx[comp[j].id]; - if (nodeIdx < minIdx) { - minIdx = nodeIdx; - minCompIdx = i; - } - } + const nodes = graph.getAllNodes(); + + // Johnson's algorithm, sort nodes + for (let i = 0; i < nodes.length; i += 1) { + const node = nodes[i]; + const nodeId = node.id; + node2Idx[nodeId] = i; + idx2Node[i] = node; + } + // If there are specified included nodes, the specified nodes are sorted first in order to end the search early + if (nodeIds && include) { + for (let i = 0; i < nodeIds.length; i++) { + const nodeId = nodeIds[i]; + node2Idx[nodes[i].id] = node2Idx[nodeId]; + node2Idx[nodeId] = 0; + idx2Node[0] = nodes.find((node) => node.id === nodeId); + idx2Node[node2Idx[nodes[i].id]] = nodes[i]; + } + } + + // Returns the adjList of the strongly connected component of the node (order > = nodeOrder) + const getMinComponentAdj = (components: INode[][]) => { + let minCompIdx; + let minIdx = Infinity; + // Find least component and the lowest node + for (let i = 0; i < components.length; i += 1) { + const comp = components[i]; + for (let j = 0; j < comp.length; j++) { + const nodeIdx = node2Idx[comp[j].id]; + if (nodeIdx < minIdx) { + minIdx = nodeIdx; + minCompIdx = i; } - const component = components[minCompIdx]; - const adjList: { [key: NodeID]: number[] } = {}; - for (let i = 0; i < component.length; i += 1) { - const node = component[i]; - adjList[node.id] = []; - for (const neighbor of graph.getRelatedEdges(node.id, "out").map(n => n.target).filter((n) => component.map(c => c.id).indexOf(n) > -1)) { - // 对自环情况 (点连向自身) 特殊处理：记录自环，但不加入adjList - if (neighbor === node.id && !(include === false && nodeIds.indexOf(node.id) > -1)) { - allCycles.push({ [node.id]: node }); - } else { - adjList[node.id].push(node2Idx[neighbor]); - } - } + } + } + const component = components[minCompIdx]; + const adjList: { [key: ID]: number[] } = {}; + for (let i = 0; i < component.length; i += 1) { + const node = component[i]; + adjList[node.id] = []; + for (const neighbor of graph + .getRelatedEdges(node.id, 'out') + .map((n) => n.target) + .filter((n) => component.map((c) => c.id).indexOf(n) > -1)) { + // 对自环情况 (点连向自身) 特殊处理：记录自环，但不加入adjList + if ( + neighbor === node.id && + !(include === false && nodeIds.indexOf(node.id) > -1) + ) { + allCycles.push({ [node.id]: node }); + } else { + adjList[node.id].push(node2Idx[neighbor]); } - return { - component, - adjList, - minIdx, - }; + } + } + return { + component, + adjList, + minIdx, }; + }; - let nodeIdx = 0; - while (nodeIdx < nodes.length) { - const sccs = detectStrongConnectComponents(graph).filter( - (component) => component.length > 1, - ); - if (sccs.length === 0) break; - const scc = getMinComponentAdj(sccs); - const { minIdx, adjList, component } = scc; - if (component.length > 1) { - component.forEach((node) => { - B[node.id] = new Set(); - }); - const startNode = idx2Node[minIdx]; - // StartNode is not in the specified node to include. End the search ahead of time. - if (nodeIds && include && nodeIds.indexOf(startNode.id) === -1) return allCycles; - circuit(startNode, startNode, adjList); - nodeIdx = minIdx + 1; - } else { - break; - } - break; + let nodeIdx = 0; + while (nodeIdx < nodes.length) { + const sccs = detectStrongConnectComponents(graph).filter( + (component) => component.length > 1 + ); + if (sccs.length === 0) break; + const scc = getMinComponentAdj(sccs); + const { minIdx, adjList, component } = scc; + if (component.length > 1) { + component.forEach((node) => { + B[node.id] = new Set(); + }); + const startNode = idx2Node[minIdx]; + // StartNode is not in the specified node to include. End the search ahead of time. + if (nodeIds && include && nodeIds.indexOf(startNode.id) === -1) + return allCycles; + circuit(startNode, startNode, adjList); + nodeIdx = minIdx + 1; + } else { + break; } - return allCycles; + break; + } + return allCycles; }; /** @@ -296,12 +327,11 @@ export const detectAllDirectedCycle = (graph: Graph, nodeIds?: NodeID[], include * @returns An array of objects representing the detected cycles in the graph. */ export const detectAllCycles = ( - graph: Graph, - directed?: boolean, - nodeIds?: string[], - include = true, + graph: Graph, + directed?: boolean, + nodeIds?: string[], + include = true ) => { - if (directed) return detectAllDirectedCycle(graph, nodeIds, include); - return detectAllUndirectedCycle(graph, nodeIds, include); + if (directed) return detectAllDirectedCycle(graph, nodeIds, include); + return detectAllUndirectedCycle(graph, nodeIds, include); }; - diff --git a/packages/graph/src/dfs.ts b/packages/graph/src/dfs.ts index 723f44c..a53896b 100644 --- a/packages/graph/src/dfs.ts +++ b/packages/graph/src/dfs.ts @@ -1,15 +1,19 @@ -import { Graph, IAlgorithmCallbacks, NodeID } from './types'; +import { ID } from '@antv/graphlib'; +import { Graph, IAlgorithmCallbacks } from './types'; /** * Initializes the callback functions for the depth-first search algorithm. * @param callbacks (Optional) The original callbacks object containing custom callback functions. * @returns The initialized callbacks object. */ -function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks) { +function initCallbacks( + callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallbacks +) { const initiatedCallback = callbacks; - const stubCallback = () => { }; + const stubCallback = () => {}; const allowTraversalCallback = () => true; - initiatedCallback.allowTraversal = callbacks.allowTraversal || allowTraversalCallback; + initiatedCallback.allowTraversal = + callbacks.allowTraversal || allowTraversalCallback; initiatedCallback.enter = callbacks.enter || stubCallback; initiatedCallback.leave = callbacks.leave || stubCallback; return initiatedCallback; @@ -27,42 +31,48 @@ function initCallbacks(callbacks: IAlgorithmCallbacks = {} as IAlgorithmCallback */ function depthFirstSearchRecursive( graph: Graph, - currentNodeId: NodeID, - previousNodeId: NodeID, + currentNodeId: ID, + previousNodeId: ID, callbacks: IAlgorithmCallbacks, - visit: Set, + visit: Set, directed: boolean, - visitOnce: boolean, + visitOnce: boolean ) { callbacks.enter({ current: currentNodeId, previous: previousNodeId, }); const neighbors = directed - ? - graph.getRelatedEdges(currentNodeId, "out").map(e => graph.getNode(e.target)) - : - graph.getNeighbors(currentNodeId) - ; + ? graph + .getRelatedEdges(currentNodeId, 'out') + .map((e) => graph.getNode(e.target)) + : graph.getNeighbors(currentNodeId); neighbors.forEach((nextNode) => { const nextNodeId = nextNode.id; // `Visit` is not considered when judging recursive conditions if ( - visitOnce ? - (callbacks.allowTraversal({ - previous: previousNodeId, - current: currentNodeId, - next: nextNodeId, - }) && !visit.has(nextNodeId)) - : - callbacks.allowTraversal({ - previous: previousNodeId, - current: currentNodeId, - next: nextNodeId, - }) + visitOnce + ? callbacks.allowTraversal({ + previous: previousNodeId, + current: currentNodeId, + next: nextNodeId, + }) && !visit.has(nextNodeId) + : callbacks.allowTraversal({ + previous: previousNodeId, + current: currentNodeId, + next: nextNodeId, + }) ) { visit.add(nextNodeId); - depthFirstSearchRecursive(graph, nextNodeId, currentNodeId, callbacks, visit, directed, visitOnce); + depthFirstSearchRecursive( + graph, + nextNodeId, + currentNodeId, + callbacks, + visit, + directed, + visitOnce + ); } }); callbacks.leave({ @@ -81,12 +91,20 @@ function depthFirstSearchRecursive( */ export function depthFirstSearch( graph: Graph, - startNodeId: NodeID, + startNodeId: ID, originalCallbacks?: IAlgorithmCallbacks, directed: boolean = false, visitOnce: boolean = true ) { - const visit = new Set(); + const visit = new Set(); visit.add(startNodeId); - depthFirstSearchRecursive(graph, startNodeId, '', initCallbacks(originalCallbacks), visit, directed, visitOnce); + depthFirstSearchRecursive( + graph, + startNodeId, + '', + initCallbacks(originalCallbacks), + visit, + directed, + visitOnce + ); } diff --git a/packages/graph/src/louvain.ts b/packages/graph/src/louvain.ts index f37ba6d..705bbb4 100644 --- a/packages/graph/src/louvain.ts +++ b/packages/graph/src/louvain.ts @@ -1,9 +1,9 @@ -import { ID, Node } from "@antv/graphlib"; -import { clone } from "@antv/util"; -import { Cluster, ClusterData, ClusterMap, Graph, NodeData } from "./types"; -import { getAllProperties, oneHot } from "./utils"; -import { graph2AdjacencyMatrix } from "./adjMatrix"; -import { Vector } from "./vector"; +import { ID, Node } from '@antv/graphlib'; +import { clone } from '@antv/util'; +import { Cluster, ClusterData, ClusterMap, Graph, NodeData } from './types'; +import { getAllProperties, oneHot } from './utils'; +import { graph2AdjacencyMatrix } from './adjMatrix'; +import { Vector } from './vector'; /** * The quality of the communities referred as partitions hereafter is measured by Modularity of the partition. @@ -13,23 +13,24 @@ function getModularity( nodes: Node[], adjMatrix: number[][], ks: number[], - m: number + m: number, + nodeToCluster: Map ) { const length = adjMatrix.length; const param = 2 * m; // number if links let modularity = 0; for (let i = 0; i < length; i++) { - const clusteri = nodes[i].data.clusterId as string; + const clusteri = nodeToCluster.get(nodes[i].id); for (let j = 0; j < length; j++) { - const clusterj = nodes[j].data.clusterId as string; + const clusterj = nodeToCluster.get(nodes[j].id); if (clusteri !== clusterj) continue; // 1 if x = y and 0 otherwise const entry = adjMatrix[i][j] || 0; // Aij: the weightof the edge between i & j const ki = ks[i] || 0; // Ki: degree of the node const kj = ks[j] || 0; - modularity += (entry - ki * kj / param); + modularity += entry - (ki * kj) / param; } } - modularity *= (1 / param); + modularity *= 1 / param; return modularity; } @@ -37,6 +38,7 @@ function getModularity( function getInertialModularity( nodes: Node[] = [], allPropertiesWeight: number[][], + nodeToCluster: Map ) { const length = nodes.length; let totalProperties = new Vector([]); @@ -51,7 +53,8 @@ function getInertialModularity( let variance: number = 0; for (let i = 0; i < length; i++) { const propertiesi = new Vector(allPropertiesWeight[i]); - const squareEuclideanDistance = propertiesi.squareEuclideanDistance(avgProperties); + const squareEuclideanDistance = + propertiesi.squareEuclideanDistance(avgProperties); variance += squareEuclideanDistance; } @@ -60,17 +63,21 @@ function getInertialModularity( nodes.forEach(() => { squareEuclideanDistanceInfo.push([]); }); + const clusterInertialMap = new Map(); for (let i = 0; i < length; i++) { const propertiesi = new Vector(allPropertiesWeight[i]); - nodes[i].data['clusterInertial'] = 0; + clusterInertialMap.set(nodes[i].id, 0); for (let j = 0; j < length; j++) { - if ( i === j) { + if (i === j) { squareEuclideanDistanceInfo[i][j] = 0; continue; } const propertiesj = new Vector(allPropertiesWeight[j]); - squareEuclideanDistanceInfo[i][j] = propertiesi.squareEuclideanDistance(propertiesj); - (nodes[i].data['clusterInertial'] as number) += squareEuclideanDistanceInfo[i][j]; + squareEuclideanDistanceInfo[i][j] = + propertiesi.squareEuclideanDistance(propertiesj); + let clusterInertial = clusterInertialMap.get(nodes[i].id); + clusterInertial += squareEuclideanDistanceInfo[i][j]; + clusterInertialMap.set(nodes[i].id, clusterInertial); } } @@ -78,12 +85,15 @@ function getInertialModularity( let inertialModularity = 0; const param = 2 * length * variance; for (let i = 0; i < length; i++) { - const clusteri = nodes[i].data.clusterId; + const clusteri = nodeToCluster.get(nodes[i].id); for (let j = 0; j < length; j++) { - const clusterj = nodes[j].data.clusterId; - if ( i === j || clusteri !== clusterj) continue; - const inertial = ((nodes[i].data.clusterInertial as number) * (nodes[j].data.clusterInertial as number)) - / Math.pow(param, 2) - squareEuclideanDistanceInfo[i][j] / param; + const clusterj = nodeToCluster.get(nodes[j].id); + if (i === j || clusteri !== clusterj) continue; + const inertial = + (clusterInertialMap.get(nodes[i].id) * + clusterInertialMap.get(nodes[j].id)) / + Math.pow(param, 2) - + squareEuclideanDistanceInfo[i][j] / param; inertialModularity += inertial; } } @@ -110,31 +120,40 @@ export function louvain( inertialModularity: boolean = false, involvedKeys: string[] = [], uninvolvedKeys: string[] = ['id'], - inertialWeight: number = 1, + inertialWeight: number = 1 ): ClusterData { const nodes = graph.getAllNodes(); const edges = graph.getAllEdges(); let allPropertiesWeight: number[][] = []; + const originIndexMap = new Map(); if (inertialModularity) { nodes.forEach((node, index) => { - node.data.originIndex = index; + originIndexMap.set(node.id, index); }); - + let nodeTypeInfo: string[] = []; if (nodes.every((node) => 'nodeType' in node.data)) { - nodeTypeInfo = Array.from(new Set(nodes.map((node) => node.data.nodeType as string))); + nodeTypeInfo = Array.from( + new Set(nodes.map((node) => node.data.nodeType as string)) + ); nodes.forEach((node) => { - node.data.nodeType = nodeTypeInfo.findIndex((nodeType) => nodeType === node.data.nodeType); + node.data.nodeType = nodeTypeInfo.findIndex( + (nodeType) => nodeType === node.data.nodeType + ); }); } // 所有节点属性集合 const properties = getAllProperties(nodes); - + // 所有节点属性one-hot特征向量集合 - allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys) as number[][]; + allPropertiesWeight = oneHot( + properties, + involvedKeys, + uninvolvedKeys + ) as number[][]; } - + /** * 1. To start with each node is assigned to a different community or partition. * The number of partitions is equal to number of nodes N. @@ -142,16 +161,17 @@ export function louvain( let uniqueId = 1; const clusters: ClusterMap = {}; const nodeMap: Record; idx: number }> = {}; + const nodeToCluster = new Map(); nodes.forEach((node, i) => { const cid: string = String(uniqueId++); - node.data.clusterId = cid; + nodeToCluster.set(node.id, cid); clusters[cid] = { id: cid, - nodes: [node] + nodes: [node], }; nodeMap[node.id] = { node, - idx: i + idx: i, }; }); // the adjacent matrix of calNodes inside clusters @@ -191,12 +211,18 @@ export function louvain( let finalNodes: Node[] = []; let finalClusters: ClusterMap = {}; while (true) { - if (inertialModularity && nodes.every((node) => node.hasOwnProperty('properties'))) { - totalModularity = getModularity(nodes, adjMatrix, ks, m) + getInertialModularity(nodes, allPropertiesWeight) * inertialWeight; + if ( + inertialModularity && + nodes.every((node) => node.hasOwnProperty('properties')) + ) { + totalModularity = + getModularity(nodes, adjMatrix, ks, m, nodeToCluster) + + getInertialModularity(nodes, allPropertiesWeight, nodeToCluster) * + inertialWeight; } else { - totalModularity = getModularity(nodes, adjMatrix, ks, m); + totalModularity = getModularity(nodes, adjMatrix, ks, m, nodeToCluster); } - + // 第一次迭代previousModularity直接赋值 if (iter === 0) { previousModularity = totalModularity; @@ -204,7 +230,10 @@ export function louvain( finalClusters = clusters; } - const increaseWithinThreshold = totalModularity > 0 && totalModularity > previousModularity && totalModularity - previousModularity < threshold; + const increaseWithinThreshold = + totalModularity > 0 && + totalModularity > previousModularity && + totalModularity - previousModularity < threshold; // 总模块度增加才更新最优解 if (totalModularity > previousModularity) { finalClusters = clone(clusters); @@ -222,20 +251,21 @@ export function louvain( let sumTot = 0; edges.forEach((edge) => { const { source, target } = edge; - const sourceClusterId = nodeMap[source].node.data.clusterId; - const targetClusterId = nodeMap[target].node.data.clusterId; - if ((sourceClusterId === clusterId && targetClusterId !== clusterId) - || (targetClusterId === clusterId && sourceClusterId !== clusterId)) { - sumTot = sumTot + (edge.data[weightPropertyName] as number || 1); + const sourceClusterId = nodeToCluster.get(source); + const targetClusterId = nodeToCluster.get(target); + if ( + (sourceClusterId === clusterId && targetClusterId !== clusterId) || + (targetClusterId === clusterId && sourceClusterId !== clusterId) + ) { + sumTot = sumTot + ((edge.data[weightPropertyName] as number) || 1); } }); clusters[clusterId].sumTot = sumTot; }); - // move the nodes to increase the delta modularity nodes.forEach((node, i) => { - const selfCluster = clusters[node.data.clusterId]; + const selfCluster = clusters[nodeToCluster.get(node.id)]; let bestIncrease = 0; let bestCluster: Cluster; @@ -251,22 +281,31 @@ export function louvain( // the modurarity for **removing** the node i from the origin cluster of node i const removeModurarity = kiin - selfCluster.sumTot * commonParam; // nodes for **removing** node i into this neighbor cluster - const selfClusterNodesAfterRemove = selfClusterNodes.filter((scNode) => scNode.id !== node.id); + const selfClusterNodesAfterRemove = selfClusterNodes.filter( + (scNode) => scNode.id !== node.id + ); const propertiesWeightRemove: number[][] = []; selfClusterNodesAfterRemove.forEach((nodeRemove, index) => { - propertiesWeightRemove[index] = allPropertiesWeight[nodeRemove.data.originIndex as number]; + propertiesWeightRemove[index] = + allPropertiesWeight[originIndexMap.get(nodeRemove.id)]; }); // the inertialModularity for **removing** the node i from the origin cluster of node i - const removeInertialModularity = inertialModularity ? getInertialModularity(selfClusterNodesAfterRemove, allPropertiesWeight) * inertialWeight : 0; + const removeInertialModularity = inertialModularity + ? getInertialModularity( + selfClusterNodesAfterRemove, + allPropertiesWeight, + nodeToCluster + ) * inertialWeight + : 0; // the neightbors of the node const nodeNeighborIds = neighbors[node.id]; Object.keys(nodeNeighborIds).forEach((neighborNodeId) => { const neighborNode = nodeMap[neighborNodeId].node; - const neighborClusterId = neighborNode.data.clusterId; + const neighborClusterId = nodeToCluster.get(neighborNode.id); // if the node and the neighbor of node are in the same cluster, reutrn - if (neighborClusterId === node.data.clusterId) return; + if (neighborClusterId === nodeToCluster.get(node.id)) return; const neighborCluster = clusters[neighborClusterId]; const clusterNodes = neighborCluster.nodes; @@ -281,20 +320,31 @@ export function louvain( }); // the modurarity for **adding** node i into this neighbor cluster - const addModurarity = neighborClusterKiin - neighborCluster.sumTot * commonParam; + const addModurarity = + neighborClusterKiin - neighborCluster.sumTot * commonParam; // nodes for **adding** node i into this neighbor cluster - const clusterNodesAfterAdd= clusterNodes.concat([node]); + const clusterNodesAfterAdd = clusterNodes.concat([node]); const propertiesWeightAdd: number[][] = []; clusterNodesAfterAdd.forEach((nodeAdd, index) => { - propertiesWeightAdd[index] = allPropertiesWeight[nodeAdd.data.originIndex as number]; + propertiesWeightAdd[index] = + allPropertiesWeight[originIndexMap.get(nodeAdd.id)]; }); // the inertialModularity for **adding** node i into this neighbor cluster - const addInertialModularity = inertialModularity ? getInertialModularity(clusterNodesAfterAdd, allPropertiesWeight) * inertialWeight : 0; + const addInertialModularity = inertialModularity + ? getInertialModularity( + clusterNodesAfterAdd, + allPropertiesWeight, + nodeToCluster + ) * inertialWeight + : 0; // the increase modurarity is the difference between addModurarity and removeModurarity let increase = addModurarity - removeModurarity; if (inertialModularity) { - increase = (addModurarity + addInertialModularity) - (removeModurarity + removeInertialModularity); + increase = + addModurarity + + addInertialModularity - + (removeModurarity + removeInertialModularity); } // find the best cluster to move node i into @@ -307,8 +357,8 @@ export function louvain( // if found a best cluster to move into if (bestIncrease > 0) { bestCluster.nodes.push(node); - const previousClusterId = node.data.clusterId; - node.data.clusterId = bestCluster.id; + const previousClusterId = nodeToCluster.get(node.id); + nodeToCluster.set(node.id, bestCluster.id); // move the node to the best cluster const nodeInSelfClusterIdx = selfCluster.nodes.indexOf(node); // remove from origin cluster @@ -319,15 +369,27 @@ export function louvain( let selfClusterSumTot = 0; edges.forEach((edge) => { const { source, target } = edge; - const sourceClusterId = nodeMap[source].node.data.clusterId; - const targetClusterId = nodeMap[target].node.data.clusterId; - if ((sourceClusterId === bestCluster.id && targetClusterId !== bestCluster.id) - || (targetClusterId === bestCluster.id && sourceClusterId !== bestCluster.id)) { - neighborClusterSumTot = neighborClusterSumTot + (edge.data[weightPropertyName] as number || 1); + const sourceClusterId = nodeToCluster.get(source); + const targetClusterId = nodeToCluster.get(target); + if ( + (sourceClusterId === bestCluster.id && + targetClusterId !== bestCluster.id) || + (targetClusterId === bestCluster.id && + sourceClusterId !== bestCluster.id) + ) { + neighborClusterSumTot = + neighborClusterSumTot + + ((edge.data[weightPropertyName] as number) || 1); } - if ((sourceClusterId === previousClusterId && targetClusterId !== previousClusterId) - || (targetClusterId === previousClusterId && sourceClusterId !== previousClusterId)) { - selfClusterSumTot = selfClusterSumTot + (edge.data[weightPropertyName] as number || 1); + if ( + (sourceClusterId === previousClusterId && + targetClusterId !== previousClusterId) || + (targetClusterId === previousClusterId && + sourceClusterId !== previousClusterId) + ) { + selfClusterSumTot = + selfClusterSumTot + + ((edge.data[weightPropertyName] as number) || 1); } }); @@ -358,12 +420,13 @@ export function louvain( finalClusters[newId] = cluster; newClusterIdMap[clusterId] = newId; delete finalClusters[clusterId]; - clusterIdx ++; + clusterIdx++; }); // restore node clusterId finalNodes.forEach((node) => { - if (node.data.clusterId && newClusterIdMap[node.data.clusterId]) { - node.data.clusterId = newClusterIdMap[node.data.clusterId]; + const clusterId = nodeToCluster.get(node.id); + if (clusterId && newClusterIdMap[clusterId]) { + nodeToCluster.set(node.id, newClusterIdMap[clusterId]); } }); // get the cluster edges @@ -374,22 +437,25 @@ export function louvain( data: { weight: number; count: number; - } + }; }[] = []; - const clusterEdgeMap: Record = {}; + > = {}; edges.forEach((edge) => { const { source, target } = edge; - const weight = edge.data[weightPropertyName] as number || 1; - const sourceClusterId = nodeMap[source].node.data.clusterId; - const targetClusterId = nodeMap[target].node.data.clusterId; + const weight = (edge.data[weightPropertyName] as number) || 1; + const sourceClusterId = nodeToCluster.get(source); + const targetClusterId = nodeToCluster.get(target); if (!sourceClusterId || !targetClusterId) return; const newEdgeId = `${sourceClusterId}---${targetClusterId}`; if (clusterEdgeMap[newEdgeId]) { @@ -402,8 +468,8 @@ export function louvain( target: targetClusterId, data: { weight, - count: 1 - } + count: 1, + }, }; clusterEdgeMap[newEdgeId] = newEdge; clusterEdges.push(newEdge); @@ -415,6 +481,7 @@ export function louvain( }); return { clusters: clustersArray, - clusterEdges + clusterEdges, + nodeToCluster, }; } diff --git a/packages/graph/src/types.ts b/packages/graph/src/types.ts index d4aeeee..df9e364 100644 --- a/packages/graph/src/types.ts +++ b/packages/graph/src/types.ts @@ -1,4 +1,4 @@ -import { Edge, Graph as IGraph, Node, PlainObject } from '@antv/graphlib'; +import { ID, Edge, Graph as IGraph, Node, PlainObject } from '@antv/graphlib'; // Map of attribute / eigenvalue distribution in dataset export interface KeyValueMap { @@ -22,6 +22,7 @@ export interface Cluster { export interface ClusterData { clusters: Cluster[]; clusterEdges: Edge[]; + nodeToCluster: Map; } export interface ClusterMap { @@ -32,17 +33,15 @@ export type Graph = IGraph; export type Matrix = number[]; export interface IAlgorithmCallbacks { - enter?: (param: { current: NodeID; previous: NodeID }) => void; - leave?: (param: { current: NodeID; previous?: NodeID }) => void; + enter?: (param: { current: ID; previous: ID }) => void; + leave?: (param: { current: ID; previous?: ID }) => void; allowTraversal?: (param: { - previous?: NodeID; - current?: NodeID; - next: NodeID; + previous?: ID; + current?: ID; + next: ID; }) => boolean; } -export type NodeID = string | number; - export type NodeSimilarity = Node & { data: { cosineSimilarity?: number; @@ -59,6 +58,6 @@ export type IEdge = Edge; export type IMSTAlgorithm = (graph: Graph, weightProps?: string) => IEdge[]; export interface IMSTAlgorithmOpt { - 'prim': IMSTAlgorithm; - 'kruskal': IMSTAlgorithm; -} \ No newline at end of file + prim: IMSTAlgorithm; + kruskal: IMSTAlgorithm; +} From db6c435d436a76b76cc4cdc69adbd5c0a9b71bb5 Mon Sep 17 00:00:00 2001 From: Cee Date: Mon, 16 Oct 2023 12:27:46 +0800 Subject: [PATCH 12/15] feat: k-means algorithm (#75) * feat: v5 algorithm k-means * fix: remove the properties param, because of the 'data' property in node * test: unit test for k-means * fix: fix lint * fix: replace vector with num array * fix: replace originIndex in data field with a map * fix: move clusterId in data field to a new map named nodeToCluster, and return it from k-means func * fix: fix lint * fix: the return value of louvain algorithm * fix: use ID from graphlib instead of NodeID --------- Co-authored-by: yuqi.pyq --- __tests__/unit/k-means.spec.ts | 391 +++++++++++++++++++++++++++++++++ __tests__/unit/louvain.spec.ts | 15 ++ __tests__/utils/data.ts | 29 +++ package.json | 2 +- packages/graph/src/index.ts | 3 +- packages/graph/src/k-means.ts | 223 +++++++++++++++++++ packages/graph/src/types.ts | 9 +- packages/graph/src/utils.ts | 26 ++- 8 files changed, 693 insertions(+), 5 deletions(-) create mode 100644 __tests__/unit/k-means.spec.ts create mode 100644 packages/graph/src/k-means.ts diff --git a/__tests__/unit/k-means.spec.ts b/__tests__/unit/k-means.spec.ts new file mode 100644 index 0000000..92b8e06 --- /dev/null +++ b/__tests__/unit/k-means.spec.ts @@ -0,0 +1,391 @@ +import { kMeans } from '../../packages/graph/src' +import propertiesGraphData from '../data/cluster-origin-properties-data.json'; +import { Graph } from "@antv/graphlib"; +import { dataPropertiesTransformer, dataLabelDataTransformer } from '../utils/data'; + + +describe('kMeans abnormal demo', () => { + it('no properties demo: ', () => { + const noPropertiesData = { + nodes: [ + { + id: 'node-0', + data: {}, + }, + { + id: 'node-1', + data: {}, + }, + { + id: 'node-2', + data: {}, + }, + { + id: 'node-3', + data: {}, + } + ], + } + const graph = new Graph(noPropertiesData); + const { clusters, clusterEdges } = kMeans(graph, 2); + expect(clusters.length).toBe(1); + expect(clusterEdges.length).toBe(0); + }); +}); + + +describe('kMeans normal demo', () => { + it('simple data demo: ', () => { + const simpleGraphData = { + nodes: [ + { + id: 'node-0', + properties: { + amount: 10, + city: '10001', + } + }, + { + id: 'node-1', + properties: { + amount: 10000, + city: '10002', + } + }, + { + id: 'node-2', + properties: { + amount: 3000, + city: '10003', + } + }, + { + id: 'node-3', + properties: { + amount: 3200, + city: '10003', + } + }, + { + id: 'node-4', + properties: { + amount: 2000, + city: '10003', + } + } + ], + edges: [ + { + id: 'edge-0', + source: 'node-0', + target: 'node-1', + }, + { + id: 'edge-1', + source: 'node-0', + target: 'node-2', + }, + { + id: 'edge-4', + source: 'node-3', + target: 'node-2', + }, + { + id: 'edge-5', + source: 'node-2', + target: 'node-1', + }, + { + id: 'edge-6', + source: 'node-4', + target: 'node-1', + }, + ] + } + const data = dataPropertiesTransformer(simpleGraphData); + const graph = new Graph(data); + const { clusters, nodeToCluster } = kMeans(graph, 3); + expect(clusters.length).toBe(3); + const nodes = graph.getAllNodes(); + + + expect(nodeToCluster.get(nodes[2].id)).toEqual(nodeToCluster.get(nodes[3].id)); + expect(nodeToCluster.get(nodes[2].id)).toEqual(nodeToCluster.get(nodes[4].id)); + }); + + + it('complex data demo: ', () => { + const data = dataLabelDataTransformer(propertiesGraphData); + const graph = new Graph(data); + const { clusters,nodeToCluster } = kMeans(graph, 3); + expect(clusters.length).toBe(3); + const nodes = graph.getAllNodes(); + expect(nodeToCluster.get(nodes[0].id)).toEqual(nodeToCluster.get(nodes[1].id)); + expect(nodeToCluster.get(nodes[0].id)).toEqual(nodeToCluster.get(nodes[2].id)); + expect(nodeToCluster.get(nodes[0].id)).toEqual(nodeToCluster.get(nodes[3].id)); + expect(nodeToCluster.get(nodes[0].id)).toEqual(nodeToCluster.get(nodes[4].id)); + expect(nodeToCluster.get(nodes[5].id)).toEqual(nodeToCluster.get(nodes[6].id)); + expect(nodeToCluster.get(nodes[5].id)).toEqual(nodeToCluster.get(nodes[7].id)); + expect(nodeToCluster.get(nodes[5].id)).toEqual(nodeToCluster.get(nodes[8].id)); + expect(nodeToCluster.get(nodes[5].id)).toEqual(nodeToCluster.get(nodes[9].id)); + expect(nodeToCluster.get(nodes[5].id)).toEqual(nodeToCluster.get(nodes[10].id)); + expect(nodeToCluster.get(nodes[11].id)).toEqual(nodeToCluster.get(nodes[12].id)); + expect(nodeToCluster.get(nodes[11].id)).toEqual(nodeToCluster.get(nodes[13].id)); + expect(nodeToCluster.get(nodes[11].id)).toEqual(nodeToCluster.get(nodes[14].id)); + expect(nodeToCluster.get(nodes[11].id)).toEqual(nodeToCluster.get(nodes[15].id)); + expect(nodeToCluster.get(nodes[11].id)).toEqual(nodeToCluster.get(nodes[16].id)); + }); + + it('demo use involvedKeys: ', () => { + const simpleGraphData = { + nodes: [ + { + id: 'node-0', + properties: { + amount: 10, + city: '10001', + } + }, + { + id: 'node-1', + properties: { + amount: 10000, + city: '10002', + } + }, + { + id: 'node-2', + properties: { + amount: 3000, + city: '10003', + } + }, + { + id: 'node-3', + properties: { + amount: 3200, + city: '10003', + } + }, + { + id: 'node-4', + properties: { + amount: 2000, + city: '10003', + } + } + ], + edges: [ + { + id: 'edge-0', + source: 'node-0', + target: 'node-1', + }, + { + id: 'edge-1', + source: 'node-0', + target: 'node-2', + }, + { + id: 'edge-4', + source: 'node-3', + target: 'node-2', + }, + { + id: 'edge-5', + source: 'node-2', + target: 'node-1', + }, + { + id: 'edge-6', + source: 'node-4', + target: 'node-1', + }, + ] + } + const data = dataPropertiesTransformer(simpleGraphData); + const involvedKeys = ['amount']; + const graph = new Graph(data); + const { clusters ,nodeToCluster} = kMeans(graph, 3, involvedKeys); + expect(clusters.length).toBe(3); + const nodes = graph.getAllNodes(); + expect(nodeToCluster.get(nodes[2].id)).toEqual(nodeToCluster.get(nodes[3].id)); + expect(nodeToCluster.get(nodes[2].id)).toEqual(nodeToCluster.get(nodes[4].id)); + }); + + it('demo use uninvolvedKeys: ', () => { + const simpleGraphData = { + nodes: [ + { + id: 'node-0', + properties: { + amount: 10, + city: '10001', + } + }, + { + id: 'node-1', + properties: { + amount: 10000, + city: '10002', + } + }, + { + id: 'node-2', + properties: { + amount: 3000, + city: '10003', + } + }, + { + id: 'node-3', + properties: { + amount: 3200, + city: '10003', + } + }, + { + id: 'node-4', + properties: { + amount: 2000, + city: '10003', + } + } + ], + edges: [ + { + id: 'edge-0', + source: 'node-0', + target: 'node-1', + }, + { + id: 'edge-1', + source: 'node-0', + target: 'node-2', + }, + { + id: 'edge-4', + source: 'node-3', + target: 'node-2', + }, + { + id: 'edge-5', + source: 'node-2', + target: 'node-1', + }, + { + id: 'edge-6', + source: 'node-4', + target: 'node-1', + }, + ] + } + const data = dataPropertiesTransformer(simpleGraphData); + const graph = new Graph(data); + const uninvolvedKeys = ['id', 'city']; + const { clusters,nodeToCluster } = kMeans(graph, 3, [], uninvolvedKeys); + expect(clusters.length).toBe(3); + const nodes = graph.getAllNodes(); data + expect(nodeToCluster.get(nodes[2].id)).toEqual(nodeToCluster.get(nodes[3].id)); + expect(nodeToCluster.get(nodes[2].id)).toEqual(nodeToCluster.get(nodes[4].id)); + }); + +}); + +describe('kMeans All properties values are numeric demo', () => { + it('all properties values are numeric demo: ', () => { + const allPropertiesValuesNumericData = { + nodes: [ + { + id: 'node-0', + properties: { + max: 1000000, + mean: 900000, + min: 800000, + } + }, + { + id: 'node-1', + properties: { + max: 1600000, + mean: 1100000, + min: 600000, + } + }, + { + id: 'node-2', + properties: { + max: 5000, + mean: 3500, + min: 2000, + } + }, + { + id: 'node-3', + properties: { + max: 9000, + mean: 7500, + min: 6000, + } + } + ], + edges: [], + } + const data = dataPropertiesTransformer(allPropertiesValuesNumericData); + const graph = new Graph(data); + const { clusters, clusterEdges,nodeToCluster } = kMeans(graph, 2); + expect(clusters.length).toBe(2); + expect(clusterEdges.length).toBe(0); + const nodes = graph.getAllNodes(); + expect(nodeToCluster.get(nodes[0].id)).toEqual(nodeToCluster.get(nodes[1].id)); + expect(nodeToCluster.get(nodes[2].id)).toEqual(nodeToCluster.get(nodes[3].id)); + }); + it('only one property and the value are numeric demo: ', () => { + const allPropertiesValuesNumericData = { + nodes: [ + { + id: 'node-0', + properties: { + num: 10, + } + }, + { + id: 'node-1', + properties: { + num: 12, + } + }, + { + id: 'node-2', + properties: { + num: 56, + } + }, + { + id: 'node-3', + properties: { + num: 300, + } + }, + { + id: 'node-4', + properties: { + num: 350, + } + } + ], + edges: [], + } + const data = dataPropertiesTransformer(allPropertiesValuesNumericData); + const graph = new Graph(data); + const { clusters, clusterEdges,nodeToCluster } = kMeans(graph, 2); + expect(clusters.length).toBe(2); + expect(clusterEdges.length).toBe(0); + const nodes = graph.getAllNodes(); + expect(nodeToCluster.get(nodes[0].id)).toEqual(nodeToCluster.get(nodes[1].id)); + expect(nodeToCluster.get(nodes[0].id)).toEqual(nodeToCluster.get(nodes[2].id)); + expect(nodeToCluster.get(nodes[3].id)).toEqual(nodeToCluster.get(nodes[4].id)); + }); + +}); + diff --git a/__tests__/unit/louvain.spec.ts b/__tests__/unit/louvain.spec.ts index 639db91..3f060af 100644 --- a/__tests__/unit/louvain.spec.ts +++ b/__tests__/unit/louvain.spec.ts @@ -73,6 +73,21 @@ describe('Louvain', () => { expect(clusteredData.clusterEdges[0].data.count).toBe(13); expect(clusteredData.clusterEdges[1].data.count).toBe(10); expect(clusteredData.clusterEdges[1].data.weight).toBe(14); + expect(clusteredData.nodeToCluster.get('0')).toBe('1'); + expect(clusteredData.nodeToCluster.get('1')).toBe('1'); + expect(clusteredData.nodeToCluster.get('2')).toBe('1'); + expect(clusteredData.nodeToCluster.get('3')).toBe('1'); + expect(clusteredData.nodeToCluster.get('4')).toBe('1'); + expect(clusteredData.nodeToCluster.get('5')).toBe('2'); + expect(clusteredData.nodeToCluster.get('6')).toBe('2'); + expect(clusteredData.nodeToCluster.get('7')).toBe('2'); + expect(clusteredData.nodeToCluster.get('8')).toBe('2'); + expect(clusteredData.nodeToCluster.get('9')).toBe('2'); + expect(clusteredData.nodeToCluster.get('10')).toBe('3'); + expect(clusteredData.nodeToCluster.get('11')).toBe('3'); + expect(clusteredData.nodeToCluster.get('12')).toBe('3'); + expect(clusteredData.nodeToCluster.get('13')).toBe('3'); + expect(clusteredData.nodeToCluster.get('14')).toBe('3'); }); // it('louvain with large graph', () => { // https://gw.alipayobjects.com/os/antvdemo/assets/data/relations.json diff --git a/__tests__/utils/data.ts b/__tests__/utils/data.ts index 5d33b89..2914ea5 100644 --- a/__tests__/utils/data.ts +++ b/__tests__/utils/data.ts @@ -21,3 +21,32 @@ export const dataTransformer = (data: { }), }; }; + +export const dataPropertiesTransformer = (data: { nodes: { id: NodeID, [key: string]: any }[], edges: { source: NodeID, target: NodeID, [key: string]: any }[] }): { nodes: INode[], edges: IEdge[] } => { + const { nodes, edges } = data; + return { + nodes: nodes.map((n) => { + const { id, properties, ...rest } = n; + return { id, data: { ...properties, ...rest } }; + }), + edges: edges.map((e, i) => { + const { id, source, target, ...rest } = e; + return { id: id ? id : `edge-${i}`, target, source, data: rest }; + }), + }; +}; + + +export const dataLabelDataTransformer = (data: { nodes: { id: NodeID, [key: string]: any }[], edges: { source: NodeID, target: NodeID, [key: string]: any }[] }): { nodes: INode[], edges: IEdge[] } => { + const { nodes, edges } = data; + return { + nodes: nodes.map((n) => { + const { id, label, data } = n; + return { id, data: { label, ...data } }; + }), + edges: edges.map((e, i) => { + const { id, source, target, ...rest } = e; + return { id: id ? id : `edge-${i}`, target, source, data: rest }; + }), + }; +}; \ No newline at end of file diff --git a/package.json b/package.json index 0c4fcca..2ee91e3 100644 --- a/package.json +++ b/package.json @@ -21,7 +21,7 @@ "build:ci": "pnpm -r run build:ci", "prepare": "husky install", "test": "jest", - "test_one": "jest ./__tests__/unit/detect-cycle.spec.ts", + "test_one": "jest ./__tests__/unit/k-means.spec.ts", "coverage": "jest --coverage", "build:site": "vite build", "deploy": "gh-pages -d site/dist", diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index ae8dce6..a02ba64 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -11,4 +11,5 @@ export * from './nodes-cosine-similarity'; export * from './gaddi'; export * from './connected-component'; export * from './mst'; -export * from './detect-cycle'; \ No newline at end of file +export * from './k-means'; +export * from './detect-cycle'; diff --git a/packages/graph/src/k-means.ts b/packages/graph/src/k-means.ts new file mode 100644 index 0000000..4ae1bdd --- /dev/null +++ b/packages/graph/src/k-means.ts @@ -0,0 +1,223 @@ +import { isEqual, uniq } from '@antv/util'; +import { Edge, ID } from '@antv/graphlib'; +import { getAllProperties, oneHot, getDistance } from './utils'; +import { Vector } from "./vector"; +import { ClusterData, DistanceType, Graph, EdgeData, Cluster } from './types'; + +/** + * Calculates the centroid based on the distance type and the given index. + * @param distanceType The distance type to use for centroid calculation. + * @param allPropertiesWeight The weight matrix of all properties. + * @param index The index of the centroid. + * @returns The centroid. + */ +const getCentroid = (distanceType: DistanceType, allPropertiesWeight: number[][], index: number) => { + let centroid: number[] = []; + switch (distanceType) { + case DistanceType.EuclideanDistance: + centroid = allPropertiesWeight[index]; + break; + default: + centroid = []; + break; + } + return centroid; +}; + +/** + * Performs the k-means clustering algorithm on a graph. + * @param graph The graph to perform clustering on. + * @param k The number of clusters. + * @param involvedKeys The keys of properties to be considered for clustering. Default is an empty array. + * @param uninvolvedKeys The keys of properties to be ignored for clustering. Default is ['id']. + * @param distanceType The distance type to use for clustering. Default is DistanceType.EuclideanDistance. + * @returns The cluster data containing the clusters and cluster edges. + */ +export const kMeans = ( + graph: Graph, + k: number = 3, + involvedKeys: string[] = [], + uninvolvedKeys: string[] = [], + distanceType: DistanceType = DistanceType.EuclideanDistance, +): ClusterData => { + const nodes = graph.getAllNodes(); + const edges = graph.getAllEdges(); + const nodeToOriginIdx = new Map(); + const nodeToCluster = new Map(); + const defaultClusterInfo: ClusterData = { + clusters: [ + { + id: "0", + nodes, + } + ], + clusterEdges: [], + nodeToCluster, + }; + + // When the distance type is Euclidean distance and there are no attributes in data, return directly + if (distanceType === DistanceType.EuclideanDistance && !nodes.every((node) => node.data)) { + return defaultClusterInfo; + } + let properties = []; + let allPropertiesWeight: number[][] = []; + if (distanceType === DistanceType.EuclideanDistance) { + properties = getAllProperties(nodes); + allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys) as number[][]; + } + if (!allPropertiesWeight.length) { + return defaultClusterInfo; + } + const allPropertiesWeightUniq = uniq(allPropertiesWeight.map((item) => item.join(''))); + // When the number of nodes or the length of the attribute set is less than k, k will be adjusted to the smallest of them + const finalK = Math.min(k, nodes.length, allPropertiesWeightUniq.length); + for (let i = 0; i < nodes.length; i++) { + nodeToOriginIdx.set(nodes[i].id, i); + } + const centroids: number[][] = []; + const centroidIndexList: number[] = []; + const clusters: Cluster[] = []; + for (let i = 0; i < finalK; i++) { + if (i === 0) { + // random choose centroid + const randomIndex = Math.floor(Math.random() * nodes.length); + switch (distanceType) { + case DistanceType.EuclideanDistance: + centroids[i] = allPropertiesWeight[randomIndex]; + break; + default: + centroids[i] = []; + break; + } + centroidIndexList.push(randomIndex); + nodeToCluster.set(nodes[randomIndex].id, `${i}`); + clusters[i] = { + id: `${i}`, + nodes: [nodes[randomIndex]] + }; + } else { + let maxDistance = -Infinity; + let maxDistanceNodeIndex = 0; + // Select the point with the farthest average distance from the existing centroid as the new centroid + for (let m = 0; m < nodes.length; m++) { + if (!centroidIndexList.includes(m)) { + let totalDistance = 0; + for (let j = 0; j < centroids.length; j++) { + // Find the distance from the node to the centroid (Euclidean distance of the default node attribute) + let distance = 0; + switch (distanceType) { + case DistanceType.EuclideanDistance: + distance = getDistance(allPropertiesWeight[nodeToOriginIdx.get(nodes[m].id)], centroids[j], distanceType); + break; + default: + break; + } + totalDistance += distance; + } + // The average distance from the node to each centroid (default Euclidean distance) + const avgDistance = totalDistance / centroids.length; + // Record the distance and node index to the farthest centroid + if (avgDistance > maxDistance && + !centroids.find((centroid) => isEqual(centroid, getCentroid(distanceType, allPropertiesWeight, nodeToOriginIdx.get(nodes[m].id))))) { + maxDistance = avgDistance; + maxDistanceNodeIndex = m; + } + } + } + centroids[i] = getCentroid(distanceType, allPropertiesWeight, maxDistanceNodeIndex); + centroidIndexList.push(maxDistanceNodeIndex); + clusters[i] = { + id: `${i}`, + nodes: [nodes[maxDistanceNodeIndex]] + }; + nodeToCluster.set(nodes[maxDistanceNodeIndex].id, `${i}`); + } + } + + + let iterations = 0; + while (true) { + for (let i = 0; i < nodes.length; i++) { + let minDistanceIndex = 0; + let minDistance = Infinity; + if (!(iterations === 0 && centroidIndexList.includes(i))) { + for (let j = 0; j < centroids.length; j++) { + let distance = 0; + switch (distanceType) { + case DistanceType.EuclideanDistance: + distance = getDistance(allPropertiesWeight[i], centroids[j], distanceType); + break; + default: + break; + } + if (distance < minDistance) { + minDistance = distance; + minDistanceIndex = j; + } + } + // delete node + const cId = nodeToCluster.get(nodes[i].id); + if (cId !== undefined) { + for (let n = clusters[Number(cId)].nodes.length - 1; n >= 0; n--) { + if (clusters[Number(cId)].nodes[n].id === nodes[i].id) { + clusters[Number(cId)].nodes.splice(n, 1); + } + } + } + // Divide the node into the class corresponding to the centroid (cluster center) with the smallest distance. + nodeToCluster.set(nodes[i].id, `${minDistanceIndex}`); + clusters[minDistanceIndex].nodes.push(nodes[i]); + } + } + // Determine if there is a centroid (cluster center) movement + let centroidsEqualAvg = false; + for (let i = 0; i < clusters.length; i++) { + const clusterNodes = clusters[i].nodes; + let totalVector = new Vector([]); + for (let j = 0; j < clusterNodes.length; j++) { + totalVector = totalVector.add(new Vector(allPropertiesWeight[nodeToOriginIdx.get(clusterNodes[j].id)])); + } + // Calculates the mean vector for each category + const avgVector = totalVector.avg(clusterNodes.length); + // If the mean vector is not equal to the centroid vector + if (!avgVector.equal(new Vector(centroids[i]))) { + centroidsEqualAvg = true; + // Move/update the centroid (cluster center) of each category to this mean vector + centroids[i] = avgVector.getArr(); + } + } + iterations++; + // Stop if each node belongs to a category and there is no centroid (cluster center) movement or the number of iterations exceeds 1000 + if (nodes.every((node) => !nodeToCluster.get(node.id)) && centroidsEqualAvg || iterations >= 1000) { + break; + } + } + + // get the cluster edges + const clusterEdges: Edge[] = []; + const clusterEdgeMap: { + [key: string]: Edge + } = {}; + let edgeIndex = 0; + edges.forEach((edge) => { + const { source, target } = edge; + const sourceClusterId = nodeToCluster.get(source); + const targetClusterId = nodeToCluster.get(target); + const newEdgeId = `${sourceClusterId}---${targetClusterId}`; + if (clusterEdgeMap[newEdgeId]) { + (clusterEdgeMap[newEdgeId].data.count as number)++; + } else { + const newEdge = { + id: edgeIndex++, + source: sourceClusterId, + target: targetClusterId, + data: { count: 1 }, + }; + clusterEdgeMap[newEdgeId] = newEdge; + clusterEdges.push(newEdge); + } + }); + + return { clusters, clusterEdges, nodeToCluster }; +}; + diff --git a/packages/graph/src/types.ts b/packages/graph/src/types.ts index df9e364..352ec9d 100644 --- a/packages/graph/src/types.ts +++ b/packages/graph/src/types.ts @@ -29,6 +29,7 @@ export interface ClusterMap { [key: string]: Cluster; } + export type Graph = IGraph; export type Matrix = number[]; @@ -58,6 +59,10 @@ export type IEdge = Edge; export type IMSTAlgorithm = (graph: Graph, weightProps?: string) => IEdge[]; export interface IMSTAlgorithmOpt { - prim: IMSTAlgorithm; - kruskal: IMSTAlgorithm; + 'prim': IMSTAlgorithm; + 'kruskal': IMSTAlgorithm; +} + +export enum DistanceType { + EuclideanDistance = 'euclideanDistance', } diff --git a/packages/graph/src/utils.ts b/packages/graph/src/utils.ts index e383a1f..93fa9a5 100644 --- a/packages/graph/src/utils.ts +++ b/packages/graph/src/utils.ts @@ -1,5 +1,6 @@ import { Node, PlainObject } from "@antv/graphlib"; -import { KeyValueMap, NodeData } from "./types"; +import { Vector } from "./vector"; +import { DistanceType, KeyValueMap, NodeData } from "./types"; import { uniq } from "@antv/util"; export const getAllProperties = (nodes: Node[]) => { @@ -77,3 +78,26 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol }); return oneHotCode; }; + +export const getDistance = (item: number[], otherItem: number[], distanceType: DistanceType = DistanceType.EuclideanDistance) => { + let distance = 0; + switch (distanceType) { + case DistanceType.EuclideanDistance: + distance = euclideanDistance(item, otherItem); + break; + default: + break; + } + return distance; +}; + + +function euclideanDistance(source: number[], target: number[]) { + if (source.length !== target.length) return 0; + let res = 0; + source.forEach((s, i) => { + res += Math.pow(s - target[i], 2); + }); + return Math.sqrt(res); +} + From 53939b0c57e8f4fa521ccb71e15b773e9c1ab33d Mon Sep 17 00:00:00 2001 From: Cee Date: Mon, 13 Nov 2023 21:59:43 +0800 Subject: [PATCH 13/15] feat: label propagation algorithm (#78) * fix: remove Vector import from utils * feat: v5 algorithmm label propagation * test: unit test of label propagation * fix: fix lint * fix: remove default export * fix: fix lint * fix: Eng annotation * fix: change plain object to map * chore: remove useless function --- .../data/label-propagation-test-data.json | 1 + __tests__/unit/label-propagation.spec.ts | 54 +++++ __tests__/utils/data.ts | 52 ++--- package.json | 2 +- packages/graph/src/detect-cycle.ts | 10 +- packages/graph/src/index.ts | 1 + packages/graph/src/label-propagation.ts | 197 ++++++++++++++++++ packages/graph/src/utils.ts | 33 ++- 8 files changed, 310 insertions(+), 40 deletions(-) create mode 100644 __tests__/data/label-propagation-test-data.json create mode 100644 __tests__/unit/label-propagation.spec.ts create mode 100644 packages/graph/src/label-propagation.ts diff --git a/__tests__/data/label-propagation-test-data.json b/__tests__/data/label-propagation-test-data.json new file mode 100644 index 0000000..505713f --- /dev/null +++ b/__tests__/data/label-propagation-test-data.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/__tests__/unit/label-propagation.spec.ts b/__tests__/unit/label-propagation.spec.ts new file mode 100644 index 0000000..78c7943 --- /dev/null +++ b/__tests__/unit/label-propagation.spec.ts @@ -0,0 +1,54 @@ +import { Graph } from "@antv/graphlib"; +import { labelPropagation } from '../../packages/graph/src'; +import { dataTransformer } from "../utils/data"; +import labelPropagationTestData from '../data/label-propagation-test-data.json'; + + +describe('label propagation', () => { + it('simple label propagation', () => { + const oldData = { + nodes: [ + { id: '0' }, { id: '1' }, { id: '2' }, { id: '3' }, { id: '4' }, + { id: '5' }, { id: '6' }, { id: '7' }, { id: '8' }, { id: '9' }, + { id: '10' }, { id: '11' }, { id: '12' }, { id: '13' }, { id: '14' }, + ], + edges: [ + { source: '0', target: '1' }, { source: '0', target: '2' }, { source: '0', target: '3' }, { source: '0', target: '4' }, + { source: '1', target: '2' }, { source: '1', target: '3' }, { source: '1', target: '4' }, + { source: '2', target: '3' }, { source: '2', target: '4' }, + { source: '3', target: '4' }, + { source: '0', target: '0' }, + { source: '0', target: '0' }, + { source: '0', target: '0' }, + + { source: '5', target: '6', weight: 5 }, { source: '5', target: '7' }, { source: '5', target: '8' }, { source: '5', target: '9' }, + { source: '6', target: '7' }, { source: '6', target: '8' }, { source: '6', target: '9' }, + { source: '7', target: '8' }, { source: '7', target: '9' }, + { source: '8', target: '9' }, + + { source: '10', target: '11' }, { source: '10', target: '12' }, { source: '10', target: '13' }, { source: '10', target: '14' }, + { source: '11', target: '12' }, { source: '11', target: '13' }, { source: '11', target: '14' }, + { source: '12', target: '13' }, { source: '12', target: '14' }, + { source: '13', target: '14', weight: 5 }, + + { source: '0', target: '5' }, + { source: '5', target: '10' }, + { source: '10', target: '0' }, + { source: '10', target: '0' }, + ] + }; + const data = dataTransformer(oldData); + const graph = new Graph(data); + const clusteredData = labelPropagation(graph, false, 'weight'); + expect(clusteredData.clusters.length).not.toBe(0); + expect(clusteredData.clusterEdges.length).not.toBe(0); + }); + + it('label propagation with large graph', () => { + const data = dataTransformer(labelPropagationTestData); + const graph = new Graph(data); + const clusteredData = labelPropagation(graph, false, 'weight'); + expect(clusteredData.clusters.length).not.toBe(0); + expect(clusteredData.clusterEdges.length).not.toBe(0); + } +}); diff --git a/__tests__/utils/data.ts b/__tests__/utils/data.ts index 2914ea5..127982f 100644 --- a/__tests__/utils/data.ts +++ b/__tests__/utils/data.ts @@ -6,8 +6,8 @@ import { INode, IEdge } from '../../packages/graph/src/types'; * @return {{nodes:INode[],edges:IEdge[]}} new data */ export const dataTransformer = (data: { - nodes: { id: ID; [key: string]: any }[]; - edges: { source: ID; target: ID; [key: string]: any }[]; + nodes: { id: ID;[key: string]: any }[]; + edges: { source: ID; target: ID;[key: string]: any }[]; }): { nodes: INode[]; edges: IEdge[] } => { const { nodes, edges } = data; return { @@ -22,31 +22,31 @@ export const dataTransformer = (data: { }; }; -export const dataPropertiesTransformer = (data: { nodes: { id: NodeID, [key: string]: any }[], edges: { source: NodeID, target: NodeID, [key: string]: any }[] }): { nodes: INode[], edges: IEdge[] } => { - const { nodes, edges } = data; - return { - nodes: nodes.map((n) => { - const { id, properties, ...rest } = n; - return { id, data: { ...properties, ...rest } }; - }), - edges: edges.map((e, i) => { - const { id, source, target, ...rest } = e; - return { id: id ? id : `edge-${i}`, target, source, data: rest }; - }), - }; +export const dataPropertiesTransformer = (data: { nodes: { id: ID, [key: string]: any }[], edges: { source: ID, target: ID, [key: string]: any }[] }): { nodes: INode[], edges: IEdge[] } => { + const { nodes, edges } = data; + return { + nodes: nodes.map((n) => { + const { id, properties, ...rest } = n; + return { id, data: { ...properties, ...rest } }; + }), + edges: edges.map((e, i) => { + const { id, source, target, ...rest } = e; + return { id: id ? id : `edge-${i}`, target, source, data: rest }; + }), + }; }; -export const dataLabelDataTransformer = (data: { nodes: { id: NodeID, [key: string]: any }[], edges: { source: NodeID, target: NodeID, [key: string]: any }[] }): { nodes: INode[], edges: IEdge[] } => { - const { nodes, edges } = data; - return { - nodes: nodes.map((n) => { - const { id, label, data } = n; - return { id, data: { label, ...data } }; - }), - edges: edges.map((e, i) => { - const { id, source, target, ...rest } = e; - return { id: id ? id : `edge-${i}`, target, source, data: rest }; - }), - }; +export const dataLabelDataTransformer = (data: { nodes: { id: ID, [key: string]: any }[], edges: { source: ID, target: ID, [key: string]: any }[] }): { nodes: INode[], edges: IEdge[] } => { + const { nodes, edges } = data; + return { + nodes: nodes.map((n) => { + const { id, label, data } = n; + return { id, data: { label, ...data } }; + }), + edges: edges.map((e, i) => { + const { id, source, target, ...rest } = e; + return { id: id ? id : `edge-${i}`, target, source, data: rest }; + }), + }; }; \ No newline at end of file diff --git a/package.json b/package.json index 2ee91e3..fea32dc 100644 --- a/package.json +++ b/package.json @@ -21,7 +21,7 @@ "build:ci": "pnpm -r run build:ci", "prepare": "husky install", "test": "jest", - "test_one": "jest ./__tests__/unit/k-means.spec.ts", + "test_one": "jest ./__tests__/unit/label-propagation.spec.ts", "coverage": "jest --coverage", "build:site": "vite build", "deploy": "gh-pages -d site/dist", diff --git a/packages/graph/src/detect-cycle.ts b/packages/graph/src/detect-cycle.ts index dbd3f59..dc3f61c 100644 --- a/packages/graph/src/detect-cycle.ts +++ b/packages/graph/src/detect-cycle.ts @@ -62,7 +62,7 @@ export const detectDirectedCycle = ( return true; }, }; - for (let key of Object.keys(unvisitedSet)) { + for (const key of Object.keys(unvisitedSet)) { depthFirstSearch(graph, key, callbacks, true, false); } return cycle; @@ -193,8 +193,9 @@ export const detectAllDirectedCycle = ( adjList: { [key: ID]: number[] } ) => { let closed = false; // whether a path is closed - if (nodeIds && include === false && nodeIds.indexOf(node.id) > -1) + if (nodeIds && !include && nodeIds.indexOf(node.id) > -1) { return closed; + } path.push(node); blocked.add(node); const neighbors = adjList[node.id]; @@ -277,7 +278,7 @@ export const detectAllDirectedCycle = ( // 对自环情况 (点连向自身) 特殊处理：记录自环，但不加入adjList if ( neighbor === node.id && - !(include === false && nodeIds.indexOf(node.id) > -1) + !(!include && nodeIds.indexOf(node.id) > -1) ) { allCycles.push({ [node.id]: node }); } else { @@ -306,8 +307,9 @@ export const detectAllDirectedCycle = ( }); const startNode = idx2Node[minIdx]; // StartNode is not in the specified node to include. End the search ahead of time. - if (nodeIds && include && nodeIds.indexOf(startNode.id) === -1) + if (nodeIds && include && nodeIds.indexOf(startNode.id) === -1) { return allCycles; + } circuit(startNode, startNode, adjList); nodeIdx = minIdx + 1; } else { diff --git a/packages/graph/src/index.ts b/packages/graph/src/index.ts index a02ba64..a270ecf 100644 --- a/packages/graph/src/index.ts +++ b/packages/graph/src/index.ts @@ -13,3 +13,4 @@ export * from './connected-component'; export * from './mst'; export * from './k-means'; export * from './detect-cycle'; +export * from './label-propagation'; \ No newline at end of file diff --git a/packages/graph/src/label-propagation.ts b/packages/graph/src/label-propagation.ts new file mode 100644 index 0000000..6adb88c --- /dev/null +++ b/packages/graph/src/label-propagation.ts @@ -0,0 +1,197 @@ +import { uniqueId } from "@antv/util"; +import { ClusterData, INode, IEdge, Graph, Matrix } from "./types"; +import { ID } from "@antv/graphlib"; + +function getAdjMatrix(graph: Graph, directed: boolean) { + const nodes = graph.getAllNodes(); + const matrix: Matrix[] = []; + // map node with index in data.nodes + const nodeMap = new Map(); + + if (!nodes) { + throw new Error("invalid nodes data!"); + } + + if (nodes) { + nodes.forEach((node, i) => { + nodeMap.set(node.id, i); + const row: number[] = []; + matrix.push(row); + }); + } + + const edges = graph.getAllEdges(); + if (edges) { + edges.forEach((edge) => { + const { source, target } = edge; + const sIndex = nodeMap.get(source); + const tIndex = nodeMap.get(target); + if ((!sIndex && sIndex !== 0) || (!tIndex && tIndex !== 0)) return; + matrix[sIndex][tIndex] = 1; + if (!directed) { + matrix[tIndex][sIndex] = 1; + } + }); + } + return matrix; +} + +/** + * Performs label propagation clustering on the given graph. + * @param graph The graph object representing the nodes and edges. + * @param directed A boolean indicating whether the graph is directed or not. Default is false. + * @param weightPropertyName The name of the property used as the weight for edges. Default is 'weight'. + * @param maxIteration The maximum number of iterations for label propagation. Default is 1000. + * @returns The clustering result including clusters, cluster edges, and node-to-cluster mapping. + */ +export const labelPropagation = ( + graph: Graph, + directed: boolean = false, + weightPropertyName: string = "weight", + maxIteration: number = 1000 +): ClusterData => { + // the origin data + const nodes = graph.getAllNodes(); + const edges = graph.getAllEdges(); + + const clusters: { [key: string]: { id: string; nodes: INode[] } } = {}; + const nodeMap: { [key: ID]: { node: INode; idx: number } } = {}; + const nodeToCluster = new Map(); + // init the clusters and nodeMap + nodes.forEach((node, i) => { + const cid: string = uniqueId(); + nodeToCluster.set(node.id, cid); + clusters[cid] = { + id: cid, + nodes: [node], + }; + nodeMap[node.id] = { + node, + idx: i, + }; + }); + + // the adjacent matrix of calNodes inside clusters + const adjMatrix = getAdjMatrix(graph, directed); + // the sum of each row in adjacent matrix + const ks = []; + /** + * neighbor nodes (id for key and weight for value) for each node + * neighbors = { + * id(node_id): { id(neighbor_1_id): weight(weight of the edge), id(neighbor_2_id): weight(weight of the edge), ... }, + * ... + * } + */ + const neighbors: Map> = new Map>(); + adjMatrix.forEach((row, i) => { + let k = 0; + const iid = nodes[i].id; + neighbors.set(iid, new Map()); + row.forEach((entry, j) => { + if (!entry) return; + k += entry; + const jid = nodes[j].id; + neighbors.get(iid).set(jid, entry); + }); + ks.push(k); + }); + + let iter = 0; + + while (iter < maxIteration) { + let changed = false; + nodes.forEach((node) => { + const neighborClusters: { [key: string]: number } = {}; + neighbors.get(node.id).forEach((neighborId, value) => { + const neighborWeight = neighbors.get(node.id).get(neighborId); + const neighborNode = nodeMap[neighborId].node; + const neighborClusterId = nodeToCluster.get(neighborNode.id); + if (!neighborClusters[neighborClusterId]) { + neighborClusters[neighborClusterId] = 0; + } + neighborClusters[neighborClusterId] += neighborWeight; + }); + // find the cluster with max weight + let maxWeight = -Infinity; + let bestClusterIds: string[] = []; + Object.keys(neighborClusters).forEach((clusterId) => { + if (maxWeight < neighborClusters[clusterId]) { + maxWeight = neighborClusters[clusterId]; + bestClusterIds = [clusterId]; + } else if (maxWeight === neighborClusters[clusterId]) { + bestClusterIds.push(clusterId); + } + }); + if ( + bestClusterIds.length === 1 && + bestClusterIds[0] === nodeToCluster.get(node.id) + ) { + return; + } + const selfClusterIdx = bestClusterIds.indexOf(nodeToCluster.get(node.id)); + if (selfClusterIdx >= 0) bestClusterIds.splice(selfClusterIdx, 1); + if (bestClusterIds && bestClusterIds.length) { + changed = true; + + // remove from origin cluster + const selfCluster = clusters[nodeToCluster.get(node.id)]; + const nodeInSelfClusterIdx = selfCluster.nodes.indexOf(node); + selfCluster.nodes.splice(nodeInSelfClusterIdx, 1); + + // move the node to the best cluster + const randomIdx = Math.floor(Math.random() * bestClusterIds.length); + const bestCluster = clusters[bestClusterIds[randomIdx]]; + bestCluster.nodes.push(node); + nodeToCluster.set(node.id, bestCluster.id); + } + }); + if (!changed) break; + iter++; + } + + // delete the empty clusters + Object.keys(clusters).forEach((clusterId) => { + const cluster = clusters[clusterId]; + if (!cluster.nodes || !cluster.nodes.length) { + delete clusters[clusterId]; + } + }); + + // get the cluster edges + const clusterEdges: IEdge[] = []; + const clusterEdgeMap: { [key: string]: IEdge } = {}; + edges.forEach((edge) => { + let i = 0; + const { source, target } = edge; + const weight = (edge.data[weightPropertyName] || 1) as number; + const sourceClusterId = nodeToCluster.get(nodeMap[source].node.id); + const targetClusterId = nodeToCluster.get(nodeMap[target].node.id); + const newEdgeId = `${sourceClusterId}---${targetClusterId}`; + if (clusterEdgeMap[newEdgeId]) { + clusterEdgeMap[newEdgeId].data.weight += weight; + (clusterEdgeMap[newEdgeId].data.count as number)++; + } else { + const newEdge = { + id: i++, + source: sourceClusterId, + target: targetClusterId, + data: { + weight, + count: 1, + }, + }; + clusterEdgeMap[newEdgeId] = newEdge; + clusterEdges.push(newEdge); + } + }); + + const clustersArray: { id: string; nodes: INode[] }[] = []; + Object.keys(clusters).forEach((clusterId) => { + clustersArray.push(clusters[clusterId]); + }); + return { + clusters: clustersArray, + clusterEdges, + nodeToCluster, + }; +}; diff --git a/packages/graph/src/utils.ts b/packages/graph/src/utils.ts index 93fa9a5..4b6b513 100644 --- a/packages/graph/src/utils.ts +++ b/packages/graph/src/utils.ts @@ -1,5 +1,4 @@ import { Node, PlainObject } from "@antv/graphlib"; -import { Vector } from "./vector"; import { DistanceType, KeyValueMap, NodeData } from "./types"; import { uniq } from "@antv/util"; @@ -11,7 +10,11 @@ export const getAllProperties = (nodes: Node[]) => { return allProperties; }; -export const getAllKeyValueMap = (dataList: PlainObject[], involvedKeys?: string[], uninvolvedKeys?: string[]) => { +export const getAllKeyValueMap = ( + dataList: PlainObject[], + involvedKeys?: string[], + uninvolvedKeys?: string[] +) => { let keys: string[] = []; // Use the specified keys when the keys participating in the calculation is specified if (involvedKeys?.length) { @@ -28,7 +31,7 @@ export const getAllKeyValueMap = (dataList: PlainObject[], involvedKeys?: string keys.forEach((key) => { const value: unknown[] = []; dataList.forEach((data) => { - if (data[key] !== undefined && data[key] !== '') { + if (data[key] !== undefined && data[key] !== "") { value.push(data[key]); } }); @@ -40,9 +43,17 @@ export const getAllKeyValueMap = (dataList: PlainObject[], involvedKeys?: string return allKeyValueMap; }; -export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvolvedKeys?: string[]) => { +export const oneHot = ( + dataList: PlainObject[], + involvedKeys?: string[], + uninvolvedKeys?: string[] +) => { // Get all attributes / features in the data and their corresponding values - const allKeyValueMap = getAllKeyValueMap(dataList, involvedKeys, uninvolvedKeys); + const allKeyValueMap = getAllKeyValueMap( + dataList, + involvedKeys, + uninvolvedKeys + ); const oneHotCode: unknown[][] = []; if (!Object.keys(allKeyValueMap).length) { return oneHotCode; @@ -50,7 +61,9 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol // Get all attribute / feature values const allValue = Object.values(allKeyValueMap); // Whether the values of all attributes / features are numerical - const isAllNumber = allValue.every((value) => value.every((item) => (typeof (item) === 'number'))); + const isAllNumber = allValue.every((value) => + value.every((item) => typeof item === "number") + ); // One-hot encode the data dataList.forEach((data, index) => { let code: unknown[] = []; @@ -79,7 +92,11 @@ export const oneHot = (dataList: PlainObject[], involvedKeys?: string[], uninvol return oneHotCode; }; -export const getDistance = (item: number[], otherItem: number[], distanceType: DistanceType = DistanceType.EuclideanDistance) => { +export const getDistance = ( + item: number[], + otherItem: number[], + distanceType: DistanceType = DistanceType.EuclideanDistance +) => { let distance = 0; switch (distanceType) { case DistanceType.EuclideanDistance: @@ -91,7 +108,6 @@ export const getDistance = (item: number[], otherItem: number[], distanceType: D return distance; }; - function euclideanDistance(source: number[], target: number[]) { if (source.length !== target.length) return 0; let res = 0; @@ -100,4 +116,3 @@ function euclideanDistance(source: number[], target: number[]) { }); return Math.sqrt(res); } - From f86b1b2e16c55b8e4fd360467cc08aa44d054d8d Mon Sep 17 00:00:00 2001 From: hustcc Date: Thu, 15 May 2025 13:50:08 +0800 Subject: [PATCH 14/15] fix: ci config file (#80) * fix: ci config file * chore: update action name --- .github/workflows/{ci.yml => build.yml} | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) rename .github/workflows/{ci.yml => build.yml} (87%) diff --git a/.github/workflows/ci.yml b/.github/workflows/build.yml similarity index 87% rename from .github/workflows/ci.yml rename to .github/workflows/build.yml index ec72c12..b15b50f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/build.yml @@ -1,15 +1,15 @@ -name: CI +name: build on: ["push", "pull_request"] jobs: - ci: - runs-on: ubuntu-20.04 + build: + runs-on: ubuntu-latest strategy: matrix: node-version: [15] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: pnpm/action-setup@v2 with: version: 7 From 4102466977de673503f49b9919ccfa31d46d3cdd Mon Sep 17 00:00:00 2001 From: hustcc Date: Thu, 15 May 2025 14:08:35 +0800 Subject: [PATCH 15/15] chore: update pack --- __tests__/unit/gaddi.spec.ts | 16 ++++++++-------- packages/graph-gpu/package.json | 4 ++-- packages/graph-wasm/package.json | 4 ++-- packages/graph/package.json | 2 +- 4 files changed, 13 insertions(+), 13 deletions(-) diff --git a/__tests__/unit/gaddi.spec.ts b/__tests__/unit/gaddi.spec.ts index 143b420..a67d4c0 100644 --- a/__tests__/unit/gaddi.spec.ts +++ b/__tests__/unit/gaddi.spec.ts @@ -2130,14 +2130,14 @@ describe('Performance: 1589 nodes G', () => { 'cluster', 'cluster' ); - console.log( - '1589 nodes graph matching 6 nodes full-connected pattern', - performance.now() - begin, - result.length - ); - result.forEach((re) => { - console.log(JSON.stringify(re)); - }); + // console.log( + // '1589 nodes graph matching 6 nodes full-connected pattern', + // performance.now() - begin, + // result.length + // ); + // result.forEach((re) => { + // console.log(JSON.stringify(re)); + // }); }); }); diff --git a/packages/graph-gpu/package.json b/packages/graph-gpu/package.json index 177e9a3..98dc741 100644 --- a/packages/graph-gpu/package.json +++ b/packages/graph-gpu/package.json @@ -1,6 +1,6 @@ { "name": "@antv/graph-gpu", - "version": "1.0.0", + "version": "0.2.0", "description": "provide common graph algorithms implemented with WebGPU", "keywords": [ "webgpu", @@ -25,7 +25,7 @@ "types": "lib/index.d.ts", "unpkg": "dist/index.min.js", "scripts": { - "build": "npm run clean && npm run build:esm && npm run build:umd", + "build": "npm run clean && npm run build:esm", "build:esm": "tsc", "build:umd": "webpack --config webpack.config.js --mode production", "dev:umd": "webpack --config webpack.dev.config.js --mode development", diff --git a/packages/graph-wasm/package.json b/packages/graph-wasm/package.json index 383fcee..14b835c 100644 --- a/packages/graph-wasm/package.json +++ b/packages/graph-wasm/package.json @@ -1,6 +1,6 @@ { "name": "@antv/graph-wasm", - "version": "0.0.1", + "version": "0.2.0", "description": "A WASM binding of rust implementation for graph algorithms.", "license": "MIT", "repository": { @@ -30,7 +30,7 @@ "build:wasm": "run-p build:wasm-st build:wasm-mt build:wasm-node", "build:esm": "tsc", "build:umd": "webpack --config webpack.config.js --mode production", - "build": "npm run clean && npm run build:wasm && npm run build:esm && npm run build:umd", + "build": "npm run clean", "clean": "rimraf dist pkg pkg-parallel pkg-node" }, "dependencies": { diff --git a/packages/graph/package.json b/packages/graph/package.json index c0e2226..3001d04 100644 --- a/packages/graph/package.json +++ b/packages/graph/package.json @@ -22,7 +22,7 @@ "scripts": { "clean": "rimraf dist lib", "dev": "webpack --config webpack.dev.config.js --mode development", - "build": "npm run clean && npm run build:esm && npm run build:umd", + "build": "npm run clean && npm run build:esm", "build:ci": "npm run build", "build:esm": "tsc", "build:umd": "webpack --config webpack.config.js --mode production",