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Ant Colony Optimization With A New Random Walk Model For Community Detection In Complex Networks

Author

Listed:
  • DI JIN

    (College of Computer Science and Technology, Jilin University, Changchun, 130012, China)

  • DAYOU LIU

    (College of Computer Science and Technology, Jilin University, Changchun, 130012, China)

  • BO YANG

    (College of Computer Science and Technology, Jilin University, Changchun, 130012, China)

  • JIE LIU

    (College of Computer Science and Technology, Jilin University, Changchun, 130012, China)

  • DONGXIAO HE

    (College of Computer Science and Technology, Jilin University, Changchun, 130012, China)

Abstract

Detecting communities from complex networks has recently triggered great interest. Aiming at this problem, a new ant colony optimization strategy building on the Markov random walks theory, which is named as MACO, is proposed in this paper. The framework of ant colony optimization is taken as the basic framework in this algorithm. In each iteration, a Markov random walk model is employed as heuristic rule; all of the ants' local solutions are aggregated to a global one through an idea of clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually this converges to a solution where the underlying community structure of the complex network will become clearly visible. The proposed MACO has been evaluated both on synthetic benchmarks and on some real-world networks, and compared with some present competing algorithms. Experimental result has shown that MACO is highly effective for discovering communities.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:acsxxx:v:14:y:2011:i:05:n:s0219525911003219
    DOI: 10.1142/S0219525911003219
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    References listed on IDEAS

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    Cited by:

    1. Seema Rani & Monica Mehrotra, 2019. "Community Detection in Social Networks: Literature Review," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 1-28, June.
    2. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    3. Ji, Junzhong & Song, Xiangjing & Liu, Chunnian & Zhang, Xiuzhen, 2013. "Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3260-3272.
    4. Xiao, Jing & Zhang, Yong-Jian & Xu, Xiao-Ke, 2018. "Convergence improvement of differential evolution for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 762-779.
    5. 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).

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