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.
Functions measuring similarity using graph edit distance.
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.
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.similarity._simrank_similarity_numpy
networkx.algorithms.similarity._simrank_similarity_python
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them