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Local modularity for community detection in complex networks

Author

Listed:
  • Xiang, Ju
  • Hu, Tao
  • Zhang, Yan
  • Hu, Ke
  • Li, Jian-Ming
  • Xu, Xiao-Ke
  • Liu, Cui-Cui
  • Chen, Shi

Abstract

Community detection is a topic of interest in the study of complex networks such as the protein–protein interaction networks and metabolic networks. In recent years, various methods were proposed to detect community structures of the networks. Here, a kind of local modularity with tunable parameter is derived from the Newman–Girvan modularity by a special self-loop strategy that depends on the community division of the networks. By the self-loop strategy, one can easily control the definition of modularity, and the resulting modularity can be optimized by using the existing modularity optimization algorithms. The local modularity is used as the target function for community detection, and a self-consistent method is proposed for the optimization of the local modularity. We analyze the behaviors of the local modularity and show the validity of the local modularity in detecting community structures on various networks.

Suggested Citation

  • Xiang, Ju & Hu, Tao & Zhang, Yan & Hu, Ke & Li, Jian-Ming & Xu, Xiao-Ke & Liu, Cui-Cui & Chen, Shi, 2016. "Local modularity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 451-459.
    • Handle: RePEc:eee:phsmap:v:443:y:2016:i:c:p:451-459
    DOI: 10.1016/j.physa.2015.09.093
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    References listed on IDEAS

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    Cited by:

    1. Ke Hu & Ju Xiang & Yun-Xia Yu & Liang Tang & Qin Xiang & Jian-Ming Li & Yong-Hong Tang & Yong-Jun Chen & Yan Zhang, 2020. "Significance-based multi-scale method for network community detection and its application in disease-gene prediction," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-24, March.
    2. Zhu, Jiajing & Liu, Yongguo & Zhang, Yun & Liu, Xiaofeng & Xiao, Yonghua & Wang, Shidong & Wu, Xindong, 2017. "Exploring anti-community structure in networks with application to incompatibility of traditional Chinese medicine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 31-43.
    3. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    4. He, Chaobo & Tang, Yong & Liu, Hai & Fei, Xiang & Li, Hanchao & Liu, Shuangyin, 2019. "A robust multi-view clustering method for community detection combining link and content information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 396-411.
    5. Jiang, Zhongzhou & Liu, Jing & Wang, Shuai, 2016. "Traveling salesman problems with PageRank Distance on complex networks reveal community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 293-302.
    6. Lu Wei & Na Liu & Junhua Chen & Jihong Sun, 2022. "Topic Evolution of Chinese COVID-19 Policies Based on Co-Occurrence Clustering Network Analysis," Sustainability, MDPI, vol. 14(4), pages 1-21, February.

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