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Mao-Guo Gong, Ling-Jun Zhang, Jing-Jing Ma, Li-Cheng Jiao. Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm[J]. Journal of Computer Science and Technology, 2012, 27(3): 455-467. DOI: 10.1007/s11390-012-1235-y
Citation: Mao-Guo Gong, Ling-Jun Zhang, Jing-Jing Ma, Li-Cheng Jiao. Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm[J]. Journal of Computer Science and Technology, 2012, 27(3): 455-467. DOI: 10.1007/s11390-012-1235-y

Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm

Funds: 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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  • Author Bio:

    Mao-Guo Gong received the B.Eng. degree in electronic engineer-ing and Ph.D. degree in electronic science and technology from Xidian University, Xi'an, China, in 2003 and 2009, respectively. Since 2006, he has been a teacher with Xidian Univer-sity.

  • Received Date: August 28, 2011
  • Revised Date: December 12, 2011
  • Published Date: May 04, 2012
  • 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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