Clean up

README.md

@@ -15,23 +15,33 @@ nx-parallel is a NetworkX backend that uses joblib for parallelization. This pro
 - [all_pairs_shortest_path](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/shortest_paths/unweighted.py#L62)
 - [all_pairs_shortest_path_length](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/shortest_paths/unweighted.py#L19)
 - [approximate_all_pairs_node_connectivity](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/approximation/connectivity.py#L14)
+- [average_clustering](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/cluster.py#L213)
+- [average_neighbor_degree](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/assortativity/neighbor_degree.py#L10)
 - [betweenness_centrality](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/centrality/betweenness.py#L20)
 - [closeness_vitality](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/vitality.py#L10)
+- [clustering](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/cluster.py#L146)
 - [cn_soundarajan_hopcroft](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/link_prediction.py#L200)
+- [colliders](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/dag.py#L37)
 - [common_neighbor_centrality](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/link_prediction.py#L158)
 - [edge_betweenness_centrality](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/centrality/betweenness.py#L103)
-- [is_reachable](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/tournament.py#L13)
+- [harmonic_centrality](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/centrality/harmonic.py#L10)
+- [is_reachable](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/tournament.py#L15)
 - [jaccard_coefficient](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/link_prediction.py#L80)
 - [johnson](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/shortest_paths/weighted.py#L251)
 - [local_efficiency](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/efficiency_measures.py#L11)
 - [node_redundancy](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/bipartite/redundancy.py#L12)
+- [number_attracting_components](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/components/attracting.py#L9)
+- [number_connected_components](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/components/connected.py#L9)
 - [number_of_isolates](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/isolate.py#L9)
+- [number_strongly_connected_components](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/components/strongly_connected.py#L9)
+- [number_weakly_connected_components](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/components/weakly_connected.py#L9)
 - [preferential_attachment](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/link_prediction.py#L133)
 - [ra_index_soundarajan_hopcroft](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/link_prediction.py#L232)
 - [resource_allocation_index](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/link_prediction.py#L55)
-- [square_clustering](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/cluster.py#L14)
-- [tournament_is_strongly_connected](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/tournament.py#L58)
-- [triangles](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/cluster.py#L76)
+- [square_clustering](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/cluster.py#L22)
+- [tournament_is_strongly_connected](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/tournament.py#L76)
+- [triangles](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/cluster.py#L84)
+- [v_structures](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/dag.py#L13)
 - [within_inter_cluster](https://github.com/networkx/nx-parallel/blob/main/nx_parallel/algorithms/link_prediction.py#L264)
 
 <details>

nx_parallel/algorithms/centrality/harmonic.py

@@ -6,7 +6,7 @@
 __all__ = ["harmonic_centrality"]
 
 
-@nxp._configure_if_nx_active(should_run=nxp.should_run_if_sparse)
+@nxp._configure_if_nx_active(should_run=nxp.should_run_if_sparse(threshold=0.3))
 def harmonic_centrality(
     G, nbunch=None, distance=None, sources=None, get_chunks="chunks"
 ):

nx_parallel/tests/test_should_run.py

@@ -53,7 +53,7 @@ def dummy_if_large(G):
 
 
 def test_should_run_if_sparse():
-    @nxp._configure_if_nx_active(should_run=nxp.should_run_if_sparse)
+    @nxp._configure_if_nx_active(should_run=nxp.should_run_if_sparse(threshold=0.4))
     def dummy_if_sparse(G):
         pass
 
@@ -63,12 +63,8 @@ def dummy_if_sparse(G):
         == "Graph too dense to benefit from parallel execution"
     )
 
-    G_sparse = nx.fast_gnp_random_graph(20, 0.3, seed=42)
+    G_sparse = nx.fast_gnp_random_graph(20, 0.2, seed=42)
     assert dummy_if_sparse.should_run(G_sparse)
-    assert (
-        dummy_if_sparse.should_run(G_sparse, threshold=0.2)
-        == "Graph too dense to benefit from parallel execution"
-    )
 
 
 @pytest.mark.parametrize("func_name", get_functions_with_should_run())

nx_parallel/utils/should_run_policies.py

@@ -14,6 +14,9 @@ def should_skip_parallel(*_):
 
 
 def should_run_if_large(G, *_):
+    if hasattr(G, "graph_object"):
+        G = G.graph_object
+
     if len(G) <= 200:
         return "Graph too small for parallel execution"
     return True
@@ -27,18 +30,21 @@ def default_should_run(*_):
     return True
 
 
-def should_run_if_sparse(G, *args, threshold=0.3, **kwargs):
-    if hasattr(G, "graph_object"):
-        G = G.graph_object
+def should_run_if_sparse(threshold=0.3):
+    def wrapper(G, *_):
+        if hasattr(G, "graph_object"):
+            G = G.graph_object
+
+        nodes = len(G)
+        # Handle trivial graphs separately to avoid division by zero
+        if nodes <= 1:
+            return "Empty graph" if nodes == 0 else "Single-node graph"
+
+        density = 2 * G.number_of_edges() / (nodes * (nodes - 1))
+        return (
+            True
+            if density <= threshold
+            else "Graph too dense to benefit from parallel execution"
+        )
 
-    nodes = len(G)
-    # Handle trivial graphs separately to avoid division by zero
-    if nodes <= 1:
-        return "Empty graph" if nodes == 0 else "Single-node graph"
-
-    density = 2 * G.number_of_edges() / (nodes * (nodes - 1))
-    return (
-        True
-        if density <= threshold
-        else "Graph too dense to benefit from parallel execution"
-    )
+    return wrapper

timing/README.md

@@ -25,63 +25,3 @@ CPU: 2 GHz Quad-Core Intel Core i5
 
 RAM: 16 GB LPDDR4X at 3733 MHz
 
-## Heatmaps
-
-### New Heatmaps (`new_heatmaps/`)
-
-betweeness_centrality
-![alt text](new_heatmaps/heatmap_betweenness_centrality_timing.png)
-
-edge_betweeness_centrality
-![alt text](new_heatmaps/heatmap_edge_betweenness_centrality_timing.png)
-
-number_of_isolates
-![alt text](new_heatmaps/heatmap_number_of_isolates_timing.png)
-
-tournament.is_reachable
-![alt text](new_heatmaps/heatmap_is_reachable_timing.png)
-
-node_redundancy
-![alt text](new_heatmaps/heatmap_node_redundancy_timing.png)
-
-## Old heatmaps (`old_heatmaps/`):
-
-all_pairs_all_shortest_paths
-![alt text](old_heatmaps/heatmap_all_pairs_all_shortest_paths_timing.png)
-
-all_pairs_bellman_ford_path
-![alt text](old_heatmaps/heatmap_all_pairs_bellman_ford_path_timing.png)
-
-all_pairs_bellman_ford_path_length
-![alt text](old_heatmaps/heatmap_all_pairs_bellman_ford_path_length_timing.png)
-
-all_pairs_dijkstra_path
-![alt text](old_heatmaps/heatmap_all_pairs_dijkstra_path_timing.png)
-
-all_pairs_dijkstra_path_length
-![alt text](old_heatmaps/heatmap_all_pairs_dijkstra_path_length_timing.png)
-
-all_pairs_node_connectivity
-![alt text](old_heatmaps/heatmap_all_pairs_node_connectivity_timing.png)
-
-all_pairs_shortest_path
-![alt text](old_heatmaps/heatmap_all_pairs_shortest_path_timing.png)
-
-all_pairs_shortest_path_length
-![alt text](old_heatmaps/heatmap_all_pairs_shortest_path_length_timing.png)
-
-closeness_vitality
-![alt text](old_heatmaps/heatmap_closeness_vitality_timing.png)
-
-is_strongly_connected
-![alt text](old_heatmaps/heatmap_is_strongly_connected_timing.png)
-
-johnson
-![alt text](old_heatmaps/heatmap_johnson_timing.png)
-
-local_efficiency
-![alt text](old_heatmaps/heatmap_local_efficiency_timing.png)
-
-square_clustering
-![alt text](old_heatmaps/heatmap_square_clustering_timing.png)
-

timing/new_heatmaps/timing_individual_function.py

@@ -14,14 +14,18 @@
 import types
 
 seed = random.Random(42)
-tournament_funcs = ["is_reachable", "tournament_is_strongly_connected"]
+tournament_funcs = ["is_reachable", "is_strongly_connected"]
 bipartite_funcs = ["node_redundancy"]
 community_funcs = [
     "ra_index_soundarajan_hopcroft",
     "cn_soundarajan_hopcroft",
     "within_inter_cluster",
 ]
-not_implemented_undirected = []
+not_implemented_undirected = [
+    "number_attracting_components",
+    "number_strongly_connected_components",
+    "number_weakly_connected_components",
+]
 
 
 def time_individual_function(