Random Walks#

cugraph.random_walks(G[, random_walks_type, ...])

Compute random walks for each nodes in 'start_vertices' and returns either a padded or a coalesced result.

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

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

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

Does neighborhood sampling, which samples nodes from a graph based on the current node's neighbors, with a corresponding fanout value at each hop.


cugraph.node2vec(G, start_vertices[, ...])

Computes random walks for each node in 'start_vertices', under the node2vec sampling framework.