Source code for networkx.generators.ego

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