cugraph.dask.sampling.node2vec_random_walks.node2vec_random_walks

cugraph.dask.sampling.node2vec_random_walks.node2vec_random_walks(input_graph, start_vertices: int | list | Series | DataFrame = None, max_depth: int = 1, p: float = 1.0, q: float = 1.0, random_state: int = None) → Tuple[Series | DataFrame, Series, int]

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. Vertices with no outgoing edges will be padded with -1.

Parameters:

input_graphcuGraph.Graph

The graph can be either directed or undirected.

start_vertices: int or list or cudf.Series or cudf.DataFrame

A single node or a list or a cudf.Series of nodes from which to run the random walks. In case of multi-column vertices it should be a cudf.DataFrame. Only supports int32 currently.

max_depth: int

The maximum depth of the random walks. If not specified, the maximum depth is set to 1. Must be a positive integer

p: float, optional (default=1.0, [0 < p])

Return factor, which represents the likelihood of backtracking to a previous node in the walk. A higher value makes it less likely to sample a previously visited node, while a lower value makes it more likely to backtrack, making the walk “local”. A positive float.

q: float, optional (default=1.0, [0 < q])

In-out factor, which represents the likelihood of visiting nodes closer or further from the outgoing node. If q > 1, the random walk is likelier to visit nodes closer to the outgoing node. If q < 1, the random walk is likelier to visit nodes further from the outgoing node. A positive float.

random_state: int, optional

Random seed to use when making sampling calls.

Returns:

vertex_pathsdask_cudf.Series or dask_cudf.DataFrame

Series containing the vertices of edges/paths in the random walk.

edge_weight_paths: dask_cudf.Series

Series containing the edge weights of edges represented by the returned vertex_paths

and

max_path_lengthint

The maximum path length.