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.
|Title of host publication||Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings|
|Number of pages||10|
|Publication status||Published - 2012|
|Event||12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 - Taormina, Italy|
Duration: 1 Sept 2012 → 5 Sept 2012
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||12th International Conference on Parallel Problem Solving from Nature, PPSN 2012|
|Period||1/09/12 → 5/09/12|
Copyright 2012 Elsevier B.V., All rights reserved.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)