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Detecting the evolving community structure in dynamic social networks

World Wide Web volume 23, pages 715–733 (2020)Cite this article

Abstract

Identifying the evolving community structure of social networks has recently drawn increasing attention. Evolutionary clustering, previously proposed to detect the evolution of clusters over time, presents a temporal smoothness framework to simultaneously maximize clustering accuracy and minimize the clustering drift between two successive time steps. Under this framework, evolving patterns of communities in dynamic networks were detected by finding the best trade-off between the clustering accuracy and temporal smoothness. However, two main drawbacks in previous methods limit the effectiveness of dynamic community detection. One is that the classic operators implemented by existing methods cannot avoid that a node is often inter-connected to most of its neighbors. The other is that those methods take it for granted that an inter-connection cannot exist between nodes clustered into the same community, which results in a limited search space. In this paper, we propose a novel multi-objective evolutionary clustering algorithm called DECS, to detect the evolving community structure in dynamic social networks. Specifically, we develop a migration operator cooperating with efficient operators to ensure that nodes and their most neighbors are grouped together, and use a genome matrix encoding the structure information of networks to expand the search space. DECS calculates the modularity based on the genome matrix as one of objectives to optimize. Experimental results on synthetic networks and real-world social networks demonstrate that DECS outperforms in both clustering accuracy and smoothness, contrasted with other state-of-the-art methods.

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Notes

  1. http://www.cs.umd.edu/hcil/VASTchallenge08/

  2. http://www.cs.cmu.edu/~enron/

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Acknowledgments

This work was supported by the National Key Research and Development Program of China (Grant No. 2016YFB0801003), MQNS (Grant No. 9201701203), MQEPS (Grant No. 9201701455), MQRSG (Grant No. 95109718), the National Natural Science Foundation of China (Grant No. 61702355 and No. 61872360), the Youth Innovation Promotion Association CAS (Grant No. 2017210), and the 2018 Collaborative Research Project (Macquarie University and CSIRO’s Data61).

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Authors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, 2109, Australia

    Fanzhen Liu, Jia Wu, Shan Xue, Jian Yang & Quanzheng Sheng

  2. CSIRO’s Data61, Sydney, NSW, 2015, Australia

    Shan Xue

  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Chuan Zhou

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  1. Fanzhen Liu
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  2. Jia Wu
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  3. Shan Xue
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  4. Chuan Zhou
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Correspondence to Chuan Zhou.

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This article belongs to the Topical Collection: Special Issue on Graph Data Management in Online Social Networks

Guest Editors: Kai Zheng, Guanfeng Liu, Mehmet A. Orgun, and Junping Du

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Liu, F., Wu, J., Xue, S. et al. Detecting the evolving community structure in dynamic social networks. World Wide Web 23, 715–733 (2020). https://doi.org/10.1007/s11280-019-00710-z

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Keywords

  • Dynamic social networks
  • Community structure
  • Evolutionary clustering
  • Migration operator