cugraph.dask.sampling.uniform_random_walks.uniform_random_walks#
- cugraph.dask.sampling.uniform_random_walks.uniform_random_walks(input_graph, start_vertices: int | list | Series | DataFrame = None, max_depth: int = 1, random_state: int = None) Tuple[Series | DataFrame, Series, int][source]#
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. Vertices with no outgoing edges will be padded with -1.
- Parameters:
- input_graphcuGraph.Graph
The graph can be either directed or undirected.
- start_verticesint 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
- max_depthint
The maximum depth of the random walks
- 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
- max_path_lengthint
The maximum path length