edge_subgraph

edge_subgraph(G, edges)

Returns a view of the subgraph induced by the specified edges.

The induced subgraph contains each edge in edges and each node incident to any of those edges.

Parameters:

GNetworkX Graph

edgesiterable

An iterable of edges. Edges not present in G are ignored.

Returns:

subgraphSubGraph View

A read-only edge-induced subgraph of G. Changes to G are reflected in the view.

Notes

To create a mutable subgraph with its own copies of nodes edges and attributes use subgraph.copy() or Graph(subgraph)

If you create a subgraph of a subgraph recursively you can end up with a chain of subgraphs that becomes very slow with about 15 nested subgraph views. Luckily the edge_subgraph filter nests nicely so you can use the original graph as G in this function to avoid chains. We do not rule out chains programmatically so that odd cases like an edge_subgraph of a restricted_view can be created.

Examples

>>> G = nx.path_graph(5)
>>> H = G.edge_subgraph([(0, 1), (3, 4)])
>>> list(H.nodes)
[0, 1, 3, 4]
>>> list(H.edges)
[(0, 1), (3, 4)]

For multi graphs, edges must include the edge keys:

>>> G = nx.MultiGraph(G)
>>> H = nx.edge_subgraph(G, [(0, 1, 0), (3, 4, 0)])
>>> list(H.edges)
[(0, 1, 0), (3, 4, 0)]

Edge attributes can be used to filter multiedges:

>>> G.add_edge(0, 1, color="blue")
1
>>> G.add_edge(0, 1, color="green", weight=10)
2
>>> H = nx.edge_subgraph(
...     G,
...     (
...         (u, v, k)
...         for u, v, k, clr in G.edges(keys=True, data="color")
...         if clr == "green"
...     ),
... )
>>> H.edges(keys=True, data=True)
MultiEdgeDataView([(0, 1, 2, {'color': 'green', 'weight': 10})])