PPSN XII - LNCS 7491-7492 CD-ROM

Community Detection Using Cooperative Co-evolutionary Differential Evolution

Qiang Huang¹, Thomas White², Guanbo Jia², Mirco Musolesi², Nil Turan³, Ke Tang⁴, Shan He^{2, 3}, John K. Heath³, and Xin Yao^{2, 4}

¹School of Software, Sun Yat-sen University, Guangzhou, China

²CERCIA, School of Computer Science, The University of Birmingham, Birmingham, B15 2TT, UK

³Center for Systems Biology, School of Biological Sciences, The University of Birmingham, Birmingham, B15 2TT, UK
s.he@cs.bham.ac.uk

⁴Nature Inspired Computation and Application Laboratory (NICAL), Department of Computer Science, University of Science and Technology of China, Hefei, Anhui 230027, China

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.

LNCS 7492, p. 235 ff.

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