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A Framework For Community Detection In Heterogeneous Multi-Relational Networks

Author

Listed:
  • XIN LIU

    (Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8552, Japan;
    CREST JST, K's Gobancho, 7, Gobancho, Chiyoda, Tokyo 102-0076, Japan;
    Department of Mathematics, Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei 430070, China)

  • WEICHU LIU

    (Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8852, Japan)

  • TSUYOSHI MURATA

    (Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8852, Japan)

  • KEN WAKITA

    (Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8552, Japan;
    CREST JST, K's Gobancho, 7, Gobancho, Chiyoda, Tokyo 102-0076, Japan)

Abstract

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.

Suggested Citation

  • Xin Liu & Weichu Liu & Tsuyoshi Murata & Ken Wakita, 2014. "A Framework For Community Detection In Heterogeneous Multi-Relational Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1-21.
    • Handle: RePEc:wsi:acsxxx:v:17:y:2014:i:06:n:s0219525914500180
    DOI: 10.1142/S0219525914500180
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    Cited by:

    1. Subhadeep Paul & Yuguo Chen, 2022. "Null Models and Community Detection in Multi-Layer Networks," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 163-217, June.

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