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Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm

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Abstract

Community structure is one of the most important properties in social networks, and community detection has received an enormous amount of attention in recent years. In dynamic networks, the communities may evolve over time so that pose more challenging tasks than in static ones. Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms. In this paper, a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks. It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method. Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.

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Authors and Affiliations

  1. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Mao-Guo Gong (Senior Member, CCF, Member, ACM, IEEE), Ling-Jun Zhang, Jing-Jing Ma & Li-Cheng Jiao (Senior Member, CCF, IEEE)

Authors
  1. Mao-Guo Gong
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  2. Ling-Jun Zhang
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  3. Jing-Jing Ma
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  4. Li-Cheng Jiao
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Corresponding author

Correspondence to Mao-Guo Gong.

Additional information

This work was supported by the National High Technology Research and Development 863 Program of China under Grant No. 2009AA12Z210, the Program for New Century Excellent Talents in University of China under Grant No. NCET-08-0811, the Program for New Scientific and Technological Star of Shaanxi Province of China under Grant No. 2010KJXX-03, and the Fundamental Research Funds for the Central Universities of China under Grant No. K50510020001.

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Gong, MG., Zhang, LJ., Ma, JJ. et al. Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm. J. Comput. Sci. Technol. 27, 455–467 (2012). https://doi.org/10.1007/s11390-012-1235-y

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  • DOI: https://doi.org/10.1007/s11390-012-1235-y

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