Sampling

Random walks

single-GPU

cugraph.uniform_random_walks(G[, ...])	Compute uniform random walks for each nodes in 'start_vertices'.
cugraph.biased_random_walks(G[, ...])	Compute biased random walks for each nodes in 'start_vertices'.
cugraph.node2vec_random_walks(G, start_vertices)	Computes random walks for each node in 'start_vertices', under the node2vec sampling framework.

multi-GPU

cugraph.dask.random_walks(input_graph[, ...])	compute random walks for each nodes in 'start_vertices' and returns a padded result along with the maximum path length.
cugraph.dask.uniform_random_walks(input_graph)	compute random walks under the uniform sampling framework for each nodes in 'start_vertices' and returns a padded result along with the maximum path length.
cugraph.dask.biased_random_walks(input_graph)	compute random walks under the biased sampling framework for each nodes in 'start_vertices' and returns a padded result along with the maximum path length.
cugraph.dask.node2vec_random_walks(input_graph)	compute random walks under the node2vec sampling framework for each nodes in 'start_vertices' and returns a padded result along with the maximum path length.

Neighbor sampling

cugraph.homogeneous_neighbor_sample(G, ...)	Performs uniform/biased neighborhood sampling, which samples nodes from a graph based on the current node's neighbors, with a corresponding fan_out value at each hop.
cugraph.heterogeneous_neighbor_sample(G, ...)	Performs uniform/biased neighborhood sampling, which samples nodes from a graph based on the current node's neighbors, with a corresponding fan_out value at each hop.