A Complex Network Clustering Algorithm based on Gene Expression Programming and Information Theory

The problem of complex network clustering has been long studied and a satisfactory solution is still lack so far. By cohesion superiority of Gene Expression Programming and the information-theoretic topology compression framework, a novel algorithm, CNC-GEP, is proposed for complex network clustering. With no prior knowledge of the number of modules in networks, CNC-GEP not only determines the optimal number of modules but also resolves how to cluster nodes into that number of modules so that the best module structure is revealed for networks. CNC-GEP is empirically evaluated by running it on two benchmark networks and the empirical results show that our algorithm CNC-GEP can correctly resolve module structure for complex networks in certain successful rate and the average accuracy is even up to 99.35% for Zachary’s karate club network. Keywordscomplex network clustering; information theory; Gene Expression Programming