Community detection using cooperative co-evolutionary differential evolution

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.