HyperGNN Analysis Framework

Advanced hypergraph dynamics for criminal case analysis

Overview

The HyperGNN Analysis Framework is a comprehensive solution for analyzing complex criminal cases using hypergraph neural networks. This repository contains the deployment configuration for both the frontend and backend components of the framework.

Architecture

The application consists of two main components:

1. Frontend: A modern React application with interactive dashboard, case analysis, and network visualization capabilities.
2. Backend: A Flask-based API that provides data retrieval, analysis, and management endpoints.

Features

• Interactive Dashboard: Real-time overview of active cases, analyzed entities, evidence items, and system performance.
• Advanced Case Analysis: Dedicated dashboard for in-depth case analysis, including timeline analysis, entity distribution, and key findings.
• Dynamic Network Visualization: Interactive hypergraph network visualization powered by D3.js, with smooth animations, zoom/pan controls, and node selection.
• Comprehensive Reporting: Generate and export detailed reports, including case summaries, network analysis, and statistical breakdowns.

Deployment

The application is configured for deployment using the deployment.yaml file, which defines the services and their configurations.

Frontend

The frontend is a static site built with React, Vite, and Tailwind CSS. It connects to the backend API for data retrieval and management.

Backend

The backend is a Flask application that provides RESTful API endpoints for data retrieval, analysis, and management. It is configured to run with Gunicorn for production deployment.

New in v1.2: HyperGraphQL API support for organization-aware repository management with GitHub integration.

Database Integration

The application is integrated with both Neon PostgreSQL and Supabase databases, with optimized schema and performance indexes for efficient data retrieval and analysis.

HyperGraphQL API

The HyperGNN framework now includes a comprehensive GraphQL API for managing hypergraph structures:

• GraphQL Endpoint: Query entities, relations, and hypergraph structures
• Organization-Aware: Multi-tenancy support with org/repo filtering
• GitHub Integration: Sync data with GitHub repository structures
• Scaling Utilities: Compress/expand repositories, scale to enterprise

See HYPERGRAPHQL_API.md for detailed API documentation.

Documentation

• HYPERGRAPHQL_API.md: Complete HyperGraphQL API documentation
• ARCHITECTURE.md: System architecture and design overview
• DEPLOYMENT.md: Deployment configuration and access information
• backend/README.md: Backend development guide

Getting Started

Development

1. Clone this repository
2. Install backend dependencies: pip install -r backend/requirements.txt
3. Start the backend: cd backend/src && python main.py
4. Access the frontend at the deployment URL
5. Test the API: curl http://localhost:5000/api/status

GraphQL Examples

# Query entities
curl -X POST http://localhost:5000/api/graphql \
  -H "Content-Type: application/json" \
  -d '{"query": "query { entities { id name entityType } }"}'

# Query hypergraph structure
curl -X POST http://localhost:5000/api/graphql \
  -H "Content-Type: application/json" \
  -d '{"query": "query { hypergraph { nodes { id degree } edges { id edgeType } } }"}'

# Get organization statistics
curl http://localhost:5000/api/organizations/org-1/stats

Testing

Run all tests:

cd backend
python -m unittest discover tests -v

License

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