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Community detection in complex networks by using membrane algorithm

Author

Listed:
  • Chuang Liu

    (School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China)

  • Linan Fan

    (School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China)

  • Zhou Liu

    (School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China)

  • Xiang Dai

    (School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China)

  • Jiamei Xu

    (School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China)

  • Baoren Chang

    (School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China)

Abstract

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Suggested Citation

  • Chuang Liu & Linan Fan & Zhou Liu & Xiang Dai & Jiamei Xu & Baoren Chang, 2018. "Community detection in complex networks by using membrane algorithm," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(01), pages 1-18, January.
    • Handle: RePEc:wsi:ijmpcx:v:29:y:2018:i:01:n:s0129183118500031
    DOI: 10.1142/S0129183118500031
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    Citations

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    Cited by:

    1. Hesamipour, Sajjad & Balafar, Mohammad Ali, 2019. "A new method for detecting communities and their centers using the Adamic/Adar Index and game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    2. Cheng, Hui-Min & Li, Si-Yan & Ning, Yi-Zi & Chen, Xing & Pan, Rui & Zhang, Zhong-Yuan, 2020. "Analysis on utilization of Beijing local roads using taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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