def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
"""
Compute the induced subgraph of neighbors centered at node n,
within a given radius.
Parameters
----------
G : cugraph.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.
n : integer
A single node
radius: integer, optional
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
Distances are counted in hops from n. Other cases are not supported.
Returns
-------
G_ego : cuGraph.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
--------
>>> M = cudf.read_csv('datasets/karate.csv',
delimiter = ' ',
dtype=['int32', 'int32', 'float32'],
header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> ego_graph = cugraph.ego_graph(G, seed, radius=2)
"""
(G, input_type) = ensure_cugraph_obj(G, nx_weight_attr="weight")
result_graph = type(G)()
if G.renumbered is True:
n = G.lookup_internal_vertex_id(cudf.Series([n]))
df, offsets = egonet_wrapper.egonet(G, n, radius)
if G.renumbered:
df = G.unrenumber(df, "src")
df = G.unrenumber(df, "dst")
if G.edgelist.weights:
result_graph.from_cudf_edgelist(
df, source="src", destination="dst", edge_attr="weight"
)
else:
result_graph.from_cudf_edgelist(df, source="src", destination="dst")
return _convert_graph_to_output_type(result_graph, input_type)
def batched_ego_graphs(G,
seeds,
radius=1,
center=True,
undirected=False,
distance=None):
"""
Compute the induced subgraph of neighbors for each node in seeds
within a given radius.
Parameters
----------
G : cugraph.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.
seeds : cudf.Series or list or cudf.DataFrame
Specifies the seeds of the induced egonet subgraphs.
radius: integer, optional
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
Distances are counted in hops from n. Other cases are not supported.
Returns
-------
ego_edge_lists : cudf.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.
"""
(G, input_type) = ensure_cugraph_obj(G, nx_weight_attr="weight")
if G.renumbered is True:
if isinstance(seeds, cudf.DataFrame):
seeds = G.lookup_internal_vertex_id(seeds, seeds.columns)
else:
seeds = G.lookup_internal_vertex_id(cudf.Series(seeds))
df, offsets = egonet_wrapper.egonet(G, seeds, radius)
if G.renumbered:
df = G.unrenumber(df, "src", preserve_order=True)
df = G.unrenumber(df, "dst", preserve_order=True)
return _convert_df_series_to_output_type(df, offsets, input_type)