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A novel community detection method based on whale optimization algorithm with evolutionary population

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Abstract

Community detection is the process of detecting communities in complex networks. Communities are important structures that can help us further study the properties of complex networks. In recent years, swarm intelligence algorithms have been applied to community detection and have achieved remarkable results. However, these existing algorithms have limited search ability and easily fall into the problem of local optima. In this paper, we propose a new community detection approach based on an improved whale optimization algorithm (WOA). The WOA is applied to a discrete symbol space in solving the community detection problem, therefore topology structure-based search strategies, adjustment and mergence policies, and evolutionary population method are designed to improve the efficiency and effectiveness of the method. Then, a whale optimization algorithm with evolutionary population for community detection (EP-WOCD) is proposed. Extensive experiments are conducted to compare the EP-WOCD with other state-of-the-art algorithms on both artificial and real-world social networks. Experimental results show that the EP-WOCD is effective and stable.

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Acknowledgements

This work is supported by the National Science Foundation of China (Nos. 61976182, 61572406, 61573292, and 61602327). Key Techniques of integrated operation and maintenance for urban rail train dispatching control system based on artificial intelligence (No. 2019YFH0097).

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Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Yunfei Feng, Hongmei Chen & Tianrui Li

  2. College of Computer Science, Sichuan University, Chengdu, 610065, China

    Chuan Luo

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  1. Yunfei Feng
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  2. Hongmei Chen
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  3. Tianrui Li
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  4. Chuan Luo
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Correspondence to Hongmei Chen.

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Feng, Y., Chen, H., Li, T. et al. A novel community detection method based on whale optimization algorithm with evolutionary population. Appl Intell 50, 2503–2522 (2020). https://doi.org/10.1007/s10489-020-01659-7

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