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Community detection by label propagation with compression of flow

Author

Listed:
  • Jihui Han

    (Complexity Science Center, Institute of Particle Physics, Central China Normal University)

  • Wei Li

    (Complexity Science Center, Institute of Particle Physics, Central China Normal University)

  • Zhu Su

    (Complexity Science Center, Institute of Particle Physics, Central China Normal University)

  • Longfeng Zhao

    (Complexity Science Center, Institute of Particle Physics, Central China Normal University)

  • Weibing Deng

    (Complexity Science Center, Institute of Particle Physics, Central China Normal University)

Abstract

The label propagation algorithm (LPA) has been proved to be a fast and effective method for detecting communities in large complex networks. However, its performance is subject to the non-stable and trivial solutions of the problem. In this paper, we propose a modified label propagation algorithm LPAf to efficiently detect community structures in networks. Instead of the majority voting rule of the basic LPA, LPAf updates the label of a node by considering the compression of a description of random walks on a network. A multi-step greedy agglomerative strategy is employed to enable LPAf to escape the local optimum. Furthermore, an incomplete update condition is also adopted to speed up the convergence. Experimental results on both synthetic and real-world networks confirm the effectiveness of our algorithm.

Suggested Citation

  • Jihui Han & Wei Li & Zhu Su & Longfeng Zhao & Weibing Deng, 2016. "Community detection by label propagation with compression of flow," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(12), pages 1-11, December.
    • Handle: RePEc:spr:eurphb:v:89:y:2016:i:12:d:10.1140_epjb_e2016-70264-6
    DOI: 10.1140/epjb/e2016-70264-6
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    Cited by:

    1. Zhang, Wenjun & Deng, Weibing & Li, Wei, 2018. "Statistical properties of links of network: A survey on the shipping lines of Worldwide Marine Transport Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 218-227.
    2. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.
    3. Longfeng Zhao & Chao Wang & Gang-Jin Wang & H. Eugene Stanley & Lin Chen, 2021. "Community detection and portfolio optimization," Papers 2112.13383, arXiv.org.

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    Keywords

    Statistical and Nonlinear Physics;

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