cugraph.batched_ego_graphs#

cugraph.batched_ego_graphs(G, seeds, radius=1, center=True, undirected=None, distance=None)[source]#

This function is deprecated.

Deprecated since 24.04. Batched support for multiple seeds will be added to ego_graph.

Compute the induced subgraph of neighbors for each node in seeds within a given radius.

Parameters:

Gcugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix: Graph or matrix object, which should contain the connectivity information. Edge weights, if present, should be single or double precision floating point values.
seedscudf.Series or list or cudf.DataFrame: Specifies the seeds of the induced egonet subgraphs.
radius: integer, optional (default=1): Include all neighbors of distance<=radius from n.
center: bool, optional: Defaults to True. False is not supported
undirected: bool, optional: Defaults to False. True is not supported
distance: key, optional (default=None): Distances are counted in hops from n. Other cases are not supported.

Returns:

ego_edge_listscudf.DataFrame or pandas.DataFrame: GPU data frame containing all induced sources identifiers, destination identifiers, edge weights
seeds_offsets: cudf.Series: Series containing the starting offset in the returned edge list for each seed.

Examples

>>> from cugraph.datasets import karate
>>> G = karate.get_graph(download=True)
>>> b_ego_graph, offsets = cugraph.batched_ego_graphs(G, seeds=[1,5],
...                                                   radius=2)