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A spiderweb model for community detection in dynamic networks

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Abstract

Community detection in dynamic networks is one of the most challenging tasks in the field of network analysis. In general, networks often evolve smoothly between successive snapshots. Therefore, the community structure detected in each snapshot should not only be of high quality but also reflect the smoothness of the variations compared with the previous snapshot. In this paper, we propose a novel incremental community-detection method named Spiderweb, which detects the community structure in each snapshot by simulating the evolution of spiderwebs. We categorize the evolutionary events of the network into three types, and then address the changed nodes and edges according to three corresponding evolution rules. In this procedure, some nodes are assigned to proper communities. Then, we construct a new subgraph for the unclassified changed nodes, and detect its communities efficiently. Finally, we merge some communities to obtain the resulting community structure. We conduct extensive experiments on both artificial networks and real-world networks to test the proposed method, and the experimental results show the superiority of the proposed method over some state-of-the-art algorithms in terms of both the quality and the temporal smoothness of the detected community structures. The proposed method provides us with a stable and promising solution for the problem of community detection in dynamic networks.

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Notes

  1. In our previous work, a parameter α is used to control the proportion of the common neighbors to the neighbors of u or v. According to the discussion of the parameter settings and the experimental results presented in [32], a setting of α = 0.5 is recommended for most networks. For the sake of simplicity, we remove the parameter α and use 0.5 directly here.

  2. The parameter β is used in our previous work, and for the same reason as α, we directly adopt the setting of β = 0.5 in this paper.

  3. http://cnerg.org

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Haijuan Yang, Jianjun Cheng, Xing Su, Wenbo Zhang, Shiyan Zhao & Xiaoyun Chen

  2. Department of Electronic Information Engineering, Lanzhou Vocational Technical College, Lanzhou, China

    Haijuan Yang

Authors
  1. Haijuan Yang
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  2. Jianjun Cheng
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  3. Xing Su
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  4. Wenbo Zhang
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  5. Shiyan Zhao
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  6. Xiaoyun Chen
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Correspondence to Jianjun Cheng or Xiaoyun Chen.

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Yang, H., Cheng, J., Su, X. et al. A spiderweb model for community detection in dynamic networks. Appl Intell 51, 5157–5188 (2021). https://doi.org/10.1007/s10489-020-02059-7

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