Module identification or community detection in complex networks has become increasingly important in many scientific fields because it provides insight into the relationship and interaction between network function and topology. In recent years, module identification algorithms based on stochastic optimization algorithms such as evolutionary algorithms have been demonstrated to be superior to other algorithms on small- to medium-scale networks. However, the scalability and resolution limit (RL) problems of these module identification algorithms have not been fully addressed, which impeded their application to real-world networks. This paper proposes a novel module identification algorithm called cooperative co-evolutionary module identification to address these two problems. The proposed algorithm employs a cooperative co-evolutionary framework to handle large-scale networks. We also incorporate a recursive partitioning scheme into the algorithm to effectively address the RL problem. The performance of our algorithm is evaluated on 12 benchmark complex networks. As a medical application, we apply our algorithm to identify disease modules that differentiate low- and high-grade glioma tumors to gain insights into the molecular mechanisms that underpin the progression of glioma. Experimental results show that the proposed algorithm has a very competitive performance compared with other state-of-the-art module identification algorithms. © 1997-2012 IEEE.
Bibliographical noteThe work of S. He was supported in part by the Royal Society International Exchanges 2011 NSFC cost share scheme under Grant IE111069, in part by the Royal Society International Exchanges under Project BIR002, in part by the Basic Research Program of Shenzhen under Grant JCYJ20130401170306880, and in part by the EU FP7-PEOPLE-2009-IRSES project through Nature Inspired Computation and its Applications under Grant 247619
- Community detection
- complex networks
- cooperation co-evolutionary
- module identification