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An adaptive population control framework for ACO-based community detection

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  • Wang, Chunyu
  • Zhang, Fan
  • Deng, Yue
  • Gao, Chao
  • Li, Xianghua
  • Wang, Zhen

Abstract

The community structure is one of the most important features of complex networks and has wide research and application prospects. To find the community structure, many researchers currently focus on natural heuristic methods, where an extraordinary swarm intelligence algorithm (i.e., the ant colony algorithm) is widely adopted to detect the potential community structures. However, the computational cost of such an algorithm is so high that it restricts the property and range of application. In this paper, we present a novel adaptive population control framework for ACO-based community discovery approaches to overcome the mentioned shortcomings. Specifically, this framework dynamically controls the number of ants based on the slope of the modularity and iterations. Such a framework is adopted in two different algorithms and we make corresponding comparison between this one and traditional ACO-based algorithms in six classical real networks and five synthetic datasets. Experiments show that ant colony algorithms with our proposed framework have evidently reduced time complexity and maintained the quality of community structure simultaneously.

Suggested Citation

  • Wang, Chunyu & Zhang, Fan & Deng, Yue & Gao, Chao & Li, Xianghua & Wang, Zhen, 2020. "An adaptive population control framework for ACO-based community detection," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920302861
    DOI: 10.1016/j.chaos.2020.109886
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    References listed on IDEAS

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    1. Di Jin & Dayou Liu & Bo Yang & Jie Liu & Dongxiao He, 2011. "Ant Colony Optimization With A New Random Walk Model For Community Detection In Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 14(05), pages 795-815.
    2. Du, Wenbo & Zhang, Mingyuan & Ying, Wen & Perc, Matjaž & Tang, Ke & Cao, Xianbin & Wu, Dapeng, 2018. "The networked evolutionary algorithm: A network science perspective," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 33-43.
    3. Borut Lužar & Zoran Levnajić & Janez Povh & Matjaž Perc, 2014. "Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-5, April.
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    Cited by:

    1. Li, Xianghua & Zhen, Xiyuan & Qi, Xin & Han, Huichun & Zhang, Long & Han, Zhen, 2023. "Dynamic community detection based on graph convolutional networks and contrastive learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Deng, Yue & Wang, Jiaxin & Gao, Chao & Li, Xianghua & Wang, Zhen & Li, Xuelong, 2021. "Assessing temporal–spatial characteristics of urban travel behaviors from multiday smart-card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).

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