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A New Modularity For Detecting One-To-Many Correspondence Of Communities In Bipartite Networks

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  • TSUYOSHI MURATA

    (Department of Computer Science, Tokyo Institute of Technology, W8-59 2-12-1 Ookayama, Meguro, Tokyo, 152-8552, Japan)

  • TOMOYUKI IKEYA

    (Department of Computer Science, Tokyo Institute of Technology, W8-59 2-12-1 Ookayama, Meguro, Tokyo, 152-8552, Japan)

Abstract

Real-world relations are often represented as bipartite networks, such as paper-author networks and event-attendee networks. Extracting dense subnetworks (communities) from bipartite networks and evaluating their qualities are practically important research topics. As the attempts for evaluating divisions of bipartite networks, Guimera and Barber propose bipartite modularities. This paper discusses the properties of these bipartite modularities and proposes another bipartite modularity that allows one-to-many correspondence of communities of different vertex types.

Suggested Citation

  • Tsuyoshi Murata & Tomoyuki Ikeya, 2010. "A New Modularity For Detecting One-To-Many Correspondence Of Communities In Bipartite Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 19-31.
    • Handle: RePEc:wsi:acsxxx:v:13:y:2010:i:01:n:s0219525910002402
    DOI: 10.1142/S0219525910002402
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    References listed on IDEAS

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    Cited by:

    1. Han, Beibei & Wei, Yingmei & Kang, Lai & Wang, Qingyong & Feng, Suru, 2022. "Attributed multiplex graph clustering: A heuristic clustering-aware network embedding approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).

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