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ComSim: A Bipartite Community Detection Algorithm Using Cycle and Node’s Similarity

  • Raphael Tackx
  • Fabien TarissanEmail author
  • Jean-Loup Guillaume
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

Abstract

This study proposes ComSim, a new algorithm to detect communities in bipartite networks. This approach generates a partition of \(\top \) nodes by relying on similarity between the nodes in terms of links towards \(\bot \) 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.

Keywords

Community detection Bipartite graph Social network 

Notes

Acknowledgements

This work is funded in part by the European Commission H2020 FETPROACT 2016–2017 program under grant 732942 (ODYCCEUS), by the ANR (French National Agency of Research) under grants ANR-15-CE38-0001 (AlgoDiv) and ANR-13-CORD-0017-01 (CODDDE), by the French program “PIA—Usages, services et contenus innovants” under grant O18062-44430 (REQUEST), and by the Ile-de-France program FUI21 under grant 16010629 (iTRAC).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Raphael Tackx
    • 1
  • Fabien Tarissan
    • 2
    Email author
  • Jean-Loup Guillaume
    • 3
  1. 1.LIP6, CNRS, Sorbonne Universités, UMR 7606ParisFrance
  2. 2.CNRS, ISP, École Normale Supérieure de Paris-Saclay, Universités Paris-SaclayParisFrance
  3. 3.L3IUniversity of La RochelleLa RochelleFrance

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