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Forward-merge branch-24.02 to branch-24.04 #4140

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Forward-merge branch-24.02 to branch-24.04 #4140

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182 changes: 140 additions & 42 deletions python/nx-cugraph/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -89,48 +89,146 @@ interface to its CUDA-based graph analytics library) and
[CuPy](https://cupy.dev/) (a GPU-accelerated array library) to NetworkX's
familiar and easy-to-use API.

Below is the list of algorithms (many listed using pylibcugraph names),
available today in pylibcugraph or implemented using CuPy, that are or will be
supported in nx-cugraph.

| feature/algo | release/target version |
| ----- | ----- |
| analyze_clustering_edge_cut | ? |
| analyze_clustering_modularity | ? |
| analyze_clustering_ratio_cut | ? |
| balanced_cut_clustering | ? |
| betweenness_centrality | 23.10 |
| bfs | ? |
| connected_components | 23.12 |
| core_number | ? |
| degree_centrality | 23.12 |
| ecg | ? |
| edge_betweenness_centrality | 23.10 |
| ego_graph | ? |
| eigenvector_centrality | 23.12 |
| get_two_hop_neighbors | ? |
| hits | 23.12 |
| in_degree_centrality | 23.12 |
| induced_subgraph | ? |
| jaccard_coefficients | ? |
| katz_centrality | 23.12 |
| k_core | ? |
| k_truss_subgraph | 23.12 |
| leiden | ? |
| louvain | 23.10 |
| node2vec | ? |
| out_degree_centrality | 23.12 |
| overlap_coefficients | ? |
| pagerank | 23.12 |
| personalized_pagerank | ? |
| sorensen_coefficients | ? |
| spectral_modularity_maximization | ? |
| sssp | 23.12 |
| strongly_connected_components | ? |
| triangle_count | ? |
| uniform_neighbor_sample | ? |
| uniform_random_walks | ? |
| weakly_connected_components | ? |
Below is the list of algorithms that are currently supported in nx-cugraph.

### Algorithms

```
bipartite
├─ basic
│ └─ is_bipartite
└─ generators
└─ complete_bipartite_graph
centrality
├─ betweenness
│ ├─ betweenness_centrality
│ └─ edge_betweenness_centrality
├─ degree_alg
│ ├─ degree_centrality
│ ├─ in_degree_centrality
│ └─ out_degree_centrality
├─ eigenvector
│ └─ eigenvector_centrality
└─ katz
└─ katz_centrality
cluster
├─ average_clustering
├─ clustering
├─ transitivity
└─ triangles
community
└─ louvain
└─ louvain_communities
components
├─ connected
│ ├─ connected_components
│ ├─ is_connected
│ ├─ node_connected_component
│ └─ number_connected_components
└─ weakly_connected
├─ is_weakly_connected
├─ number_weakly_connected_components
└─ weakly_connected_components
core
├─ core_number
└─ k_truss
dag
├─ ancestors
└─ descendants
isolate
├─ is_isolate
├─ isolates
└─ number_of_isolates
link_analysis
├─ hits_alg
│ └─ hits
└─ pagerank_alg
└─ pagerank
operators
└─ unary
├─ complement
└─ reverse
reciprocity
├─ overall_reciprocity
└─ reciprocity
shortest_paths
└─ unweighted
├─ single_source_shortest_path_length
└─ single_target_shortest_path_length
traversal
└─ breadth_first_search
├─ bfs_edges
├─ bfs_layers
├─ bfs_predecessors
├─ bfs_successors
├─ bfs_tree
├─ descendants_at_distance
└─ generic_bfs_edges
tree
└─ recognition
├─ is_arborescence
├─ is_branching
├─ is_forest
└─ is_tree
```

### Generators

```
classic
├─ barbell_graph
├─ circular_ladder_graph
├─ complete_graph
├─ complete_multipartite_graph
├─ cycle_graph
├─ empty_graph
├─ ladder_graph
├─ lollipop_graph
├─ null_graph
├─ path_graph
├─ star_graph
├─ tadpole_graph
├─ trivial_graph
├─ turan_graph
└─ wheel_graph
community
└─ caveman_graph
small
├─ bull_graph
├─ chvatal_graph
├─ cubical_graph
├─ desargues_graph
├─ diamond_graph
├─ dodecahedral_graph
├─ frucht_graph
├─ heawood_graph
├─ house_graph
├─ house_x_graph
├─ icosahedral_graph
├─ krackhardt_kite_graph
├─ moebius_kantor_graph
├─ octahedral_graph
├─ pappus_graph
├─ petersen_graph
├─ sedgewick_maze_graph
├─ tetrahedral_graph
├─ truncated_cube_graph
├─ truncated_tetrahedron_graph
└─ tutte_graph
social
├─ davis_southern_women_graph
├─ florentine_families_graph
├─ karate_club_graph
└─ les_miserables_graph
```

### Other

```
convert_matrix
├─ from_pandas_edgelist
└─ from_scipy_sparse_array
```

To request nx-cugraph backend support for a NetworkX API that is not listed
above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph).
4 changes: 3 additions & 1 deletion python/nx-cugraph/nx_cugraph/scripts/print_tree.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,7 +133,9 @@ def main(
}
if by == "networkx_path":
G = create_tree(path_to_info, by="networkx_path", **kwargs)
text = re.sub(r"[A-Za-z_\./]+\.", "", ("\n".join(nx.generate_network_text(G))))
text = re.sub(
r" [A-Za-z_\./]+\.", " ", ("\n".join(nx.generate_network_text(G)))
)
elif by == "plc":
G = create_tree(
path_to_info, by=["plc", "networkx_path"], prefix="plc-", **kwargs
Expand Down
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