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Comparing local modularity optimization for detecting communities in networks

Author

Listed:
  • Ju Xiang

    (Neuroscience Research Center, Changsha Medical University, Changsha 410219, Hunan, P. R. China)

  • Zhi-Zhong Wang

    (South City College, Hunan First Normal University, Changsha 410205, Hunan, P. R. China)

  • Hui-Jia Li

    (School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100080, P. R. China4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • Yan Zhang

    (Department of Computer Science, Changsha Medical University, Changsha 410219, Hunan, P. R. China)

  • Shi Chen

    (Department of Computer Science, Changsha Medical University, Changsha 410219, Hunan, P. R. China)

  • Cui-Cui Liu

    (Department of Computer Science, Changsha Medical University, Changsha 410219, Hunan, P. R. China)

  • Jian-Ming Li

    (Neuroscience Research Center, Changsha Medical University, Changsha 410219, Hunan, P. R. China)

  • Li-Juan Guo

    (Department of Basic Medical Sciences, Changsha Medical University, Changsha 410219, Hunan, P. R. China)

Abstract

Community detection is one important problem in network theory, and many methods have been proposed for detecting community structures in the networks. Given quality functions for evaluating community structures, community detection can be considered as one kind of optimization problem, such as modularity optimization, therefore, optimization of quality functions has been one of the most popular strategies for community detection. In this paper, we introduced two kinds of local modularity functions for community detection, and the self-consistent method is introduced to optimize the local modularity for detecting communities in the networks. We analyze the behaviors of the modularity optimizations, and compare the performance of them in community detection. The results confirm the superiority of the local modularity in detecting community structures, especially on large-size and heterogeneous networks.

Suggested Citation

  • Ju Xiang & Zhi-Zhong Wang & Hui-Jia Li & Yan Zhang & Shi Chen & Cui-Cui Liu & Jian-Ming Li & Li-Juan Guo, 2017. "Comparing local modularity optimization for detecting communities in networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(06), pages 1-11, June.
    • Handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:06:n:s012918311750084x
    DOI: 10.1142/S012918311750084X
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