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