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| """ | |
| Ego graph. | |
| """ | |
| __all__ = ["ego_graph"] | |
| import networkx as nx | |
| 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 | |