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An Influence-Based Label Propagation Algorithm for Overlapping Community Detection

Author

Listed:
  • Hao Xu

    (College of Computer and Information Science, Southwest University, Chongqing 400700, China)

  • Yuan Ran

    (Hanhong College, Southwest University, Chongqing 400700, China)

  • Junqian Xing

    (Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia)

  • Li Tao

    (College of Computer and Information Science, Southwest University, Chongqing 400700, China)

Abstract

Of the various characteristics of network structure, the community structure has received the most research attention. In social networks, communities are divided into overlapping communities and disjoint communities. The former are closer to the actual situation of real society than the latter, making it necessary to explore a more effective overlapping community detection algorithm. The label propagation algorithm (LPA) has been widely used in large-scale data owing to its low time cost. In the traditional LPA, all of the nodes are regarded as equivalent relationships. In this case, unreliable nodes reduce the accuracy of label propagation. To solve this problem, we propose the influence-based community overlap propagation algorithm (INF-COPRA) for ranking the influence of nodes and labels. To control the propagation process and prevent error propagation, the algorithm only provides influential nodes with labels in the initialization phase, and those labels with high influence are preferred in the propagation process. Lastly, the accuracy of INF-COPRA and existing algorithms is compared on benchmark networks and real networks. The experimental results show that the INF-COPRA algorithm significantly improves the extentded modularity (EQ) and normal mutual information (NMI) of the community, indicating that it can outperform state-of-art methods in overlapping community detection tasks.

Suggested Citation

  • Hao Xu & Yuan Ran & Junqian Xing & Li Tao, 2023. "An Influence-Based Label Propagation Algorithm for Overlapping Community Detection," Mathematics, MDPI, vol. 11(9), pages 1-17, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2133-:d:1138248
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    References listed on IDEAS

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    Cited by:

    1. Ying Song & Zhiwen Zheng & Yunmei Shi & Bo Wang, 2023. "GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks," Mathematics, MDPI, vol. 11(15), pages 1-16, July.

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