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Community detection in complex networks using edge-deleting with restrictions

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  • Chen, Xiangtao
  • Li, Juan

Abstract

Community detection is an important task with great practical significance for understanding the structure and function of complex networks in various fields. As the real world networks become larger and more complex, it is a challenge to achieve high quality of community partitioning. In order to identify community structure more effectively in complex networks, a new algorithm, which iteratively deletes edges with restrictions is proposed in this paper. The algorithm first makes use of the connection strength between vertices to divide the original network into some strongly connected communities with optimal modularity by the improved edge-deleting process, and finally reconnects the isolated vertices to initial communities for optimizing community structure. Experiments on the real-world and synthetic networks prove that the proposed algorithm achieves a competitive performance compared with other reference algorithms.

Suggested Citation

  • Chen, Xiangtao & Li, Juan, 2019. "Community detection in complex networks using edge-deleting with restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 181-194.
    • Handle: RePEc:eee:phsmap:v:519:y:2019:i:c:p:181-194
    DOI: 10.1016/j.physa.2018.12.023
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    References listed on IDEAS

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    1. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
    2. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
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    Cited by:

    1. Xinyu Wang & Liang Zhao & Ning Zhang & Liu Feng & Haibo Lin, 2022. "Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network," Papers 2204.06692, arXiv.org.
    2. Yanjie Xu & Tao Ren & Shixiang Sun, 2022. "Community Detection Based on Node Influence and Similarity of Nodes," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    3. Fei Ma & Huifeng Xue & Kum Fai Yuen & Qipeng Sun & Shumei Zhao & Yanxia Zhang & Kai Huang, 2020. "Assessing the Vulnerability of Logistics Service Supply Chain Based on Complex Network," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    4. Wang, Tao & Chen, Shanshan & Wang, Xiaoxia & Wang, Jinfang, 2020. "Label propagation algorithm based on node importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    5. Pierre Bertrand & Michel Broniatowski & Jean-François Marcotorchino, 2022. "Independence versus indetermination: basis of two canonical clustering criteria," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 1069-1093, December.
    6. Lai, Xin & Bai, Shuliang & Lin, Yong, 2022. "Normalized discrete Ricci flow used in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).

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