cugraph.ego_graph#

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

Compute the induced subgraph of neighbors centered at node n, 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.

Deprecated since version 24.12: Accepting a networkx.Graph is deprecated and will be removed in a future version. For networkx.Graph use networkx directly with the nx-cugraph backend. See: https://rapids.ai/nx-cugraph/
ninteger or list, cudf.Series, cudf.DataFrame: A single node as integer or a cudf.DataFrame if nodes are represented with multiple columns. If a cudf.DataFrame is provided, only the first row is taken as the node input.
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: This parameter is here for NetworkX compatibility and is ignored
distance: key, optional (default=None): This parameter is here for NetworkX compatibility and is ignored

Returns:

G_egocuGraph.Graph or networkx.Graph: A graph descriptor with a minimum spanning tree or forest. The networkx graph will not have all attributes copied over

Examples

>>> from cugraph.datasets import karate
>>> G = karate.get_graph(download=True)
>>> ego_graph = cugraph.ego_graph(G, 1, radius=2)