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A link clustering based memetic algorithm for overlapping community detection

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  • Li, Mingming
  • Liu, Jing

Abstract

Community detection has attracted plenty of attention in the field of complex networks recently, since communities often play important roles in networked systems. Overlapping communities are one of the characteristics of social networks, describing the phenomenon that a node may belong to more than one social group. Thus, it is necessary to find overlapping community structures for realistic social network analyses. In this paper, we propose a link clustering based memetic algorithm for detecting overlapping communities. Since links usually represent the unique relationships among nodes, link clustering can find link groups with the same characteristics. As a result, nodes are naturally partitioned into multiple communities. The proposed algorithm optimizes a modularity density function which is able to identify densely connected groups of links on the weighted line graph modeling the network, and then maps link communities to node communities based on a novel genotype representation. In our method, the number of communities can be automatically determined. Experimental results on general and sparse networks show that our method can successfully detect overlapping community structures and almost all the overlapping nodes.

Suggested Citation

  • Li, Mingming & Liu, Jing, 2018. "A link clustering based memetic algorithm for overlapping community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 410-423.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:410-423
    DOI: 10.1016/j.physa.2018.02.133
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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.
    2. Jiang, Zhongzhou & Liu, Jing & Wang, Shuai, 2016. "Traveling salesman problems with PageRank Distance on complex networks reveal community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 293-302.
    3. Stephen Kelley & Mark Goldberg & Malik Magdon-Ismail & Konstantin Mertsalov & Al Wallace, 2012. "Defining and Discovering Communities in Social Networks," Springer Optimization and Its Applications, in: My T. Thai & Panos M. Pardalos (ed.), Handbook of Optimization in Complex Networks, edition 1, chapter 0, pages 139-168, Springer.
    4. Li, Zhangtao & Liu, Jing, 2016. "A multi-agent genetic algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 336-347.
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    Cited by:

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