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A Scheme to Design Community Detection Algorithms in Various Networks

Author

Listed:
  • Haoye Lu

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Amiya Nayak

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

Abstract

Network structures, consisting of nodes and edges, have applications in almost all subjects. A set of nodes is called a community if the nodes have strong interrelations. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services. However, most detection algorithms are derived independently, which is arduous and even unnecessary. Although recent research shows that a general detection method that serves all purposes does not exist, we believe that there is some general procedure of deriving detection algorithms. In this paper, we represent such a general scheme. We mainly focus on two types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a method to define and detect community structures. Finally, we also give a demonstration to show how our design scheme works.

Suggested Citation

  • Haoye Lu & Amiya Nayak, 2019. "A Scheme to Design Community Detection Algorithms in Various Networks," Future Internet, MDPI, vol. 11(2), pages 1-17, February.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:2:p:41-:d:205150
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    References listed on IDEAS

    as
    1. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    2. Li, Hui-Jia & Bu, Zhan & Li, Yulong & Zhang, Zhongyuan & Chu, Yanchang & Li, Guijun & Cao, Jie, 2018. "Evolving the attribute flow for dynamical clustering in signed networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 20-27.
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