Community#

EgoNet#

single-GPU#

cugraph.ego_graph(G, n[, radius, center, ...])

Compute the induced subgraph of neighbors centered at node n, within a given radius.

multi-GPU#

cugraph.dask.community.egonet

Ensemble clustering for graphs (ECG)#

single-GPU#

cugraph.ecg(input_graph[, min_weight, ...])

Compute the Ensemble Clustering for Graphs (ECG) partition of the input graph.

multi-GPU#

cugraph.dask.community.ecg.ecg(input_graph)

Compute the Ensemble Clustering for Graphs (ECG) partition of the input graph.

K-Truss#

single-GPU#

cugraph.k_truss(G, k)

Returns the K-Truss subgraph of a graph for a specific k.

cugraph.ktruss_subgraph(G, k[, use_weights])

Returns the K-Truss subgraph of a graph for a specific k.

multi_GPU#

cugraph.dask.community.ktruss_subgraph.ktruss_subgraph(...)

Returns the K-Truss subgraph of a graph for a specific k.

Leiden#

single-GPU#

cugraph.leiden(G[, max_iter, resolution, ...])

Compute the modularity optimizing partition of the input graph using the Leiden algorithm

multi-GPU#

cugraph.dask.community.leiden.leiden(input_graph)

Compute the modularity optimizing partition of the input graph using the Leiden method

Leiden (MG)#

cugraph.dask.community.leiden.leiden(input_graph)

Compute the modularity optimizing partition of the input graph using the Leiden method

Louvain#

single-GPU#

cugraph.louvain(G[, max_level, max_iter, ...])

Compute the modularity optimizing partition of the input graph using the Louvain method

multi-GPU#

cugraph.dask.community.louvain.louvain(...)

Compute the modularity optimizing partition of the input graph using the Louvain method

Spectral Clustering#

cugraph.analyzeClustering_edge_cut(G, ...[, ...])

Compute the edge cut score for a partitioning/clustering The assumption is that “clustering” is the results from a call from a special clustering algorithm and contains columns named “vertex” and “cluster”.

cugraph.analyzeClustering_modularity(G, ...)

Compute the modularity score for a given partitioning/clustering.

cugraph.analyzeClustering_ratio_cut(G, ...)

Compute the ratio cut score for a partitioning/clustering

cugraph.spectralBalancedCutClustering(G, ...)

Compute a clustering/partitioning of the given graph using the spectral balanced cut method.

cugraph.spectralModularityMaximizationClustering(G, ...)

Compute a clustering/partitioning of the given graph using the spectral modularity maximization method.

Induced Subgraph#

single-GPU#

cugraph.induced_subgraph(G, vertices[, offsets])

Compute a subgraph of the existing graph including only the specified vertices.

multi-GPU#

cugraph.dask.community.induced_subgraph.induced_subgraph(...)

Compute a subgraph of the existing graph including only the specified vertices.

Triangle Counting#

single-GPU#

cugraph.triangle_count(G[, start_list])

Compute the number of triangles (cycles of length three) in the input graph.

multi-GPU#

cugraph.dask.community.triangle_count.triangle_count(...)

Computes the number of triangles (cycles of length three) and the number per vertex in the input graph.