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Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

Author

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  • Ji, Junzhong
  • Song, Xiangjing
  • Liu, Chunnian
  • Zhang, Xiuzhen

Abstract

Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:15:p:3260-3272
    DOI: 10.1016/j.physa.2013.04.001
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
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    3. 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.
    4. Faqeeh, Ali & Aghababaei Samani, Keivan, 2012. "Community detection based on the “clumpiness” matrix in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2463-2474.
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    Cited by:

    1. de Andrade, Lúcio Pereira & Espíndola, Rogério Pinto & Pereira, Gilberto Carvalho & Ebecken, Nelson Francisco Favilla, 2016. "Fuzzy modeling of plankton networks," Ecological Modelling, Elsevier, vol. 337(C), pages 149-155.
    2. Zhou, Xu & Liu, Yanheng & Zhang, Jindong & Liu, Tuming & Zhang, Di, 2015. "An ant colony based algorithm for overlapping community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 289-301.
    3. 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.
    4. Fu, Yu-Hsiang & Huang, Chung-Yuan & Sun, Chuen-Tsai, 2016. "Using a two-phase evolutionary framework to select multiple network spreaders based on community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 840-853.
    5. Malek Khojasteh Nejad, 2014. "Clustering Stock Exchange data by Using Evolutionary Algorithms for Portfolio Management," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 55-66.

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