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Multiobjective local search for community detection in networks

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Abstract

Detecting communities is of great importance in the study of complex networks. In this study, the community detection problem is formulated as a multiobjective optimization problem; then a local search-based multiobjective optimization algorithm is proposed. In the proposed algorithm, different objectivewise local searches are designed for different objectives. These simple but effective local searches cooperate to simultaneously optimize two objectives. Extensive experiments on both synthetic and real-world networks show that the proposed algorithm obtains better or competitive results compared with existing state-of-the-art algorithms.

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Acknowledgments

This work was supported in part by Zhujiang New Star Program of Science and Technology in Guangzhou (2012J2200085), Excellent Young Teachers Training Program in Guangdong Colleges and Universities (Yqgdufe1404), Program for Characteristic Innovation Talents of Guangdong (2014KTSCX127), and the National Natural Science Foundation of China (61472453, U1401256).

Conflict of interest

The authors declare that they have no conflict of interest. This research does not involve any human participant or animal and thus has no informed consent.

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Authors and Affiliations

  1. College of Information, Guangdong University of Finance and Economics, Guangzhou, People’s Republic of China

    Yalan Zhou

  2. Department of Computer Science, Sun Yat-sen University, Guangzhou, People’s Republic of China

    Jiahai Wang & Ningbo Luo

  3. School of Mobile Information Engineering, Sun Yat-sen University, Guangzhou, People’s Republic of China

    Zizhen Zhang

Authors
  1. Yalan Zhou
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  2. Jiahai Wang
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  3. Ningbo Luo
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  4. Zizhen Zhang
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Corresponding author

Correspondence to Jiahai Wang.

Additional information

Communicated by V. Loia.

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Zhou, Y., Wang, J., Luo, N. et al. Multiobjective local search for community detection in networks. Soft Comput 20, 3273–3282 (2016). https://doi.org/10.1007/s00500-015-1706-5

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  • DOI: https://doi.org/10.1007/s00500-015-1706-5

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