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

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Complex Networks & Their Applications VI (COMPLEX NETWORKS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

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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.

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Notes

  1. 1.

    We use a similar definition of \(N_\bot (v)\) for \(v\in \bot \).

  2. 2.

    Depending on the similarity function used, the projection might result in a directed weighted graph if \(\theta \) is not symmetric.

  3. 3.

    For an homogeneous analysis, we removed all TV shows and documentaries and kept only the 7 first actors listed in the casting.

  4. 4.

    Since lpBRIM does not scale up to the size of IMDb, we avoid mentioning this approach in the rest of the study.

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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).

Author information

Authors and Affiliations

  1. LIP6, CNRS, Sorbonne Universités, UMR 7606, Paris, France

    Raphael Tackx

  2. CNRS, ISP, École Normale Supérieure de Paris-Saclay, Universités Paris-Saclay, Paris, France

    Fabien Tarissan

  3. L3I, University of La Rochelle, La Rochelle, France

    Jean-Loup Guillaume

Authors
  1. Raphael Tackx
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  2. Fabien Tarissan
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  3. Jean-Loup Guillaume
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Corresponding author

Correspondence to Fabien Tarissan .

Editor information

Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Tackx, R., Tarissan, F., Guillaume, JL. (2018). ComSim: A Bipartite Community Detection Algorithm Using Cycle and Node’s Similarity. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72149-1

  • Online ISBN: 978-3-319-72150-7

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