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Graph similarity algorithms based on NetworkX.

Project description


Graph similarity algorithms based on NetworkX.

BSD License


$ pip install -U graphsim


>>> import graphsim as gs

Supported algorithms

  • gs.ascos: Asymmetric network Structure COntext Similarity, by Hung-Hsuan Chen et al.

  • gs.nsim_bvd04: node-node similarity matrix, by Blondel et al.

  • gs.hits: the hub and authority scores for nodes, by Kleinberg.

  • gs.nsim_hs03: node-node similarity with mismatch penalty, by Heymans et al.

  • gs.simrank: A Measure of Structural-Context Similarity, by Jeh et al.

  • gs.simrank_bipartite: SimRank for bipartite graphs, by Jeh et al.

  • gs.tacsim: Topology-Attributes Coupling Similarity, by Xiaming Chen et al.

  • gs.tacsim_self: calculate the self-similarity via TACSim.

  • gs.tacsim_combined: A combined topology-attributes coupling similarity, by Xiaming Chen et al.

  • gs.tacsim_in_C: an efficient implementation of TACSim in pure C.

  • gs.tacsim_self_in_C: an efficient implementation of self-similarity via TACSim in C.

Supported utilities

  • gs.normalized: L2 normalization of vectors, matrices or arrays.

  • gs.node_edge_adjacency: Obtain node-edge adjacency matrices in source and dest directions.


Xiaming Chen <>

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