# Community#

## EgoNet#

 `cugraph.batched_ego_graphs`(G, seeds[, ...]) This function is deprecated. `cugraph.ego_graph`(G, n[, radius, center, ...]) Compute the induced subgraph of neighbors centered at node n, within a given radius.

## Ensemble clustering for graphs (ECG)#

 `cugraph.ecg`(input_graph[, min_weight, ...]) Compute the Ensemble Clustering for Graphs (ECG) partition of the input graph.

## K-Truss#

 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.

## Leiden#

 `cugraph.leiden`(G[, max_iter, resolution, ...]) Compute the modularity optimizing partition of the input graph using the Leiden algorithm

## Louvain#

 `cugraph.louvain`(G[, max_level, max_iter, ...]) Compute the modularity optimizing partition of the input graph using the Louvain method

## Louvain (MG)#

 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”. Compute the modularity score for a given partitioning/clustering. Compute the ratio cut score for a partitioning/clustering Compute a clustering/partitioning of the given graph using the spectral balanced cut method. Compute a clustering/partitioning of the given graph using the spectral modularity maximization method.

## Subgraph Extraction#

 `cugraph.subgraph`(G, vertices) Compute a subgraph of the existing graph including only the specified vertices.

## Triangle Counting#

 `cugraph.triangle_count`(G[, start_list]) Compute the number of triangles (cycles of length three) in the input graph.