Community detection using cooperative co-evolutionary differential evolution

  • Authors:

  • Qiang Huang;Thomas White;Guanbo Jia;Mirco Musolesi;Nil Turan;Ke Tang;Shan He;John K. Heath;Xin Yao

  • Affiliations:

  • School of Software, Sun Yat-sen University, Guangzhou, China;CERCIA, School of Computer Science, The University of Birmingham, Birmingham, UK;CERCIA, School of Computer Science, The University of Birmingham, Birmingham, UK;CERCIA, School of Computer Science, The University of Birmingham, Birmingham, UK;Center for Systems Biology, School of Biological Sciences, The University of Birmingham, Birmingham, UK;Nature Inspired Computation and Application Laboratory (NICAL), Department of Computer Science, University of Science and Technology of China, Hefei, Anhui, China;CERCIA, School of Computer Science, The University of Birmingham, Birmingham, UK,Center for Systems Biology, School of Biological Sciences, The University of Birmingham, Birmingham, UK;Center for Systems Biology, School of Biological Sciences, The University of Birmingham, Birmingham, UK;CERCIA, School of Computer Science, The University of Birmingham, Birmingham, UK,Nature Inspired Computation and Application Laboratory (NICAL), Department of Computer Science, University of Scien ...

  • Venue:
  • PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
  • Year:
  • 2012

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Abstract

In many scientific fields, from biology to sociology, community detection in complex networks has become increasingly important. This paper, for the first time, introduces Cooperative Co-evolution framework for detecting communities in complex networks. A Bias Grouping scheme is proposed to dynamically decompose a complex network into smaller subnetworks to handle large-scale networks. We adopt Differential Evolution (DE) to optimize network modularity to search for an optimal partition of a network. We also design a novel mutation operator specifically for community detection. The resulting algorithm, Cooperative Co-evolutionary DE based Community Detection (CCDECD) is evaluated on 5 small to large scale real-world social and biological networks. Experimental results show that CCDECD has very competitive performance compared with other state-of-the-art community detection algorithms.