"""
Ego graph.
"""
__all__ = ["ego_graph"]
import networkx as nx
[docs]def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
"""Returns induced subgraph of neighbors centered at node n within
a given radius.
Parameters
----------
G : graph
A NetworkX Graph or DiGraph
n : node
A single node
radius : number, optional
Include all neighbors of distance<=radius from n.
center : bool, optional
If False, do not include center node in graph
undirected : bool, optional
If True use both in- and out-neighbors of directed graphs.
distance : key, optional
Use specified edge data key as distance. For example, setting
distance='weight' will use the edge weight to measure the
distance from the node n.
Notes
-----
For directed graphs D this produces the "out" neighborhood
or successors. If you want the neighborhood of predecessors
first reverse the graph with D.reverse(). If you want both
directions use the keyword argument undirected=True.
Node, edge, and graph attributes are copied to the returned subgraph.
"""
if undirected:
if distance is not None:
sp, _ = nx.single_source_dijkstra(
G.to_undirected(), n, cutoff=radius, weight=distance
)
else:
sp = dict(
nx.single_source_shortest_path_length(
G.to_undirected(), n, cutoff=radius
)
)
else:
if distance is not None:
sp, _ = nx.single_source_dijkstra(G, n, cutoff=radius, weight=distance)
else:
sp = dict(nx.single_source_shortest_path_length(G, n, cutoff=radius))
H = G.subgraph(sp).copy()
if not center:
H.remove_node(n)
return H