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Community Detection Based on Node Influence and Similarity of Nodes

Author

Listed:
  • Yanjie Xu

    (Software College, Northeastern University, Shenyang 110169, China)

  • Tao Ren

    (Software College, Northeastern University, Shenyang 110169, China)

  • Shixiang Sun

    (Software College, Northeastern University, Shenyang 110169, China)

Abstract

Community detection is a fundamental topic in network science, with a variety of applications. However, there are still fundamental questions about how to detect more realistic network community structures. To address this problem and considering the structure of a network, we propose an agglomerative community detection algorithm, which is based on node influence and the similarity of nodes. The proposed algorithm consists of three essential steps: identifying the central node based on node influence, selecting a candidate neighbor to expand the community based on the similarity of nodes, and merging the small community based on the similarity of communities. The performance and effectiveness of the proposed algorithm were tested on real and synthetic networks, and they were further evaluated through modularity and NMI anlaysis. The experimental results show that the proposed algorithm is effective in community detection and it is quite comparable to existing classic methods.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:970-:d:774029
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    References listed on IDEAS

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    7. Agrawal, Smita & Patel, Atul, 2021. "SAG Cluster: An unsupervised graph clustering based on collaborative similarity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    Full references (including those not matched with items on IDEAS)

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