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New Random Walk Algorithm Based on Different Seed Nodes for Community Detection

Author

Listed:
  • Jiansheng Cai

    (School of Mathematics and Statistics, Weifang University, Dongfeng East Street, Weifang 261061, China
    These authors contributed equally to this work.)

  • Wencong Li

    (School of Mathematical Sciences, Ocean University of China, Songling Road, Qingdao 266100, China
    These authors contributed equally to this work.)

  • Xiaodong Zhang

    (School of Mathematical Sciences, MOE-LSC, SHL-MAC, Shanghai Jiao Tong University, Dongchuan Road, Shanghai 200240, China)

  • Jihui Wang

    (School of Mathematical Sciences, University of Jinan, West Nanxinzhuang Road, Jinan 250022, China)

Abstract

A complex network is an abstract modeling of complex systems in the real world, which plays an important role in analyzing the function of complex systems. Community detection is an important tool for analyzing network structure. In this paper, we propose a new community detection algorithm (RWBS) based on different seed nodes which aims to understand the community structure of the network, which provides a new idea for the allocation of resources in the network. RWBS provides a new centrality metric ( M C ) to calculate node importance, which calculates the ranking of nodes as seed nodes. Furthermore, two algorithms are proposed for determining seed nodes on networks with and without ground truth, respectively. We set the number of steps for the random walk to six according to the six degrees of separation theory to reduce the running time of the algorithm. Since some traditional community detection algorithms may detect smaller communities, e.g., two nodes become one community, this may make the resource allocation unreasonable. Therefore, modularity ( Q ) is chosen as the optimization function to combine communities, which can improve the quality of detected communities. Final experimental results on real-world and synthetic networks show that the RWBS algorithm can effectively detect communities.

Suggested Citation

  • Jiansheng Cai & Wencong Li & Xiaodong Zhang & Jihui Wang, 2024. "New Random Walk Algorithm Based on Different Seed Nodes for Community Detection," Mathematics, MDPI, vol. 12(15), pages 1-21, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2374-:d:1446203
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    References listed on IDEAS

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    5. Jiao, Qing-Ju & Huang, Yan & Shen, Hong-Bin, 2015. "Community mining with new node similarity by incorporating both global and local topological knowledge in a constrained random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 363-371.
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