This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.
Source code for networkx.generators.ego
""" Ego graph. """ # Copyright (C) 2010 by # Aric Hagberg <email@example.com> # Dan Schult <firstname.lastname@example.org> # Pieter Swart <email@example.com> # All rights reserved. # BSD license. __author__ = """\n""".join(['Drew Conway <firstname.lastname@example.org>', 'Aric Hagberg <email@example.com>']) __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=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=nx.single_source_shortest_path_length(G,n,cutoff=radius) H=G.subgraph(sp).copy() if not center: H.remove_node(n) return H