ComSim : A bipartite community detection algorithm using cycle and node's similarity

Raphael Tackx 1 Fabien Tarissan Jean-Loup Guillaume
1 ComplexNetworks
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This study proposes ComSim, a new algorithm to detect communities in bipartite networks. This approach generates a partition of nodes by relying on similarity between the nodes in terms of links towards ⊥ nodes. In order to show the relevance of this approach, we implemented and tested the algorithm on 2 small datasets equipped with a ground-truth partition of the nodes. It turns out that, compared to 3 baseline algorithms used in the context of bipartite graph, ComSim proposes the best communities. In addition, we tested the algorithm on a large scale network. Results show that ComSim has good performances, close in time to Louvain. Besides, a qualitative investigation of the communities detected by ComSim reveals that it proposes more balanced communities.
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Raphael Tackx, Fabien Tarissan, Jean-Loup Guillaume. ComSim : A bipartite community detection algorithm using cycle and node's similarity. Complex Networks 2017, Nov 2017, Lyon, France. pp.278-289, ⟨10.1007/978-3-319-72150-7_23⟩. ⟨hal-01657093⟩

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