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Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

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Similarity Measures

Functions measuring similarity using graph edit distance.

The graph edit distance is the number of edge/node changes needed to make two graphs isomorphic.

The default algorithm/implementation is sub-optimal for some graphs. The problem of finding the exact Graph Edit Distance (GED) is NP-hard so it is often slow. If the simple interface graph_edit_distance takes too long for your graph, try optimize_graph_edit_distance and/or optimize_edit_paths.

At the same time, I encourage capable people to investigate alternative GED algorithms, in order to improve the choices available.

graph_edit_distance(G1, G2[, node_match, …])	Returns GED (graph edit distance) between graphs G1 and G2.
optimal_edit_paths(G1, G2[, node_match, …])	Returns all minimum-cost edit paths transforming G1 to G2.
optimize_graph_edit_distance(G1, G2[, …])	Returns consecutive approximations of GED (graph edit distance) between graphs G1 and G2.
optimize_edit_paths(G1, G2[, node_match, …])	GED (graph edit distance) calculation: advanced interface.
simrank_similarity(G[, source, target, …])	Returns the SimRank similarity of nodes in the graph G.
simrank_similarity_numpy(G[, source, …])	Calculate SimRank of nodes in G using matrices with numpy.