| """ |
| Ego graph. |
| """ |
|
|
| __all__ = ["ego_graph"] |
|
|
| import networkx as nx |
|
|
|
|
| @nx._dispatchable(preserve_all_attrs=True, returns_graph=True) |
| 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 = nx.single_source_shortest_path_length(G, n, cutoff=radius) |
|
|
| H = G.subgraph(sp).copy() |
| if not center: |
| H.remove_node(n) |
| return H |
|
|
