| Summary: | Community detection is a key aspect for understanding network structures and uncovers the underlying functions or characteristics of complex systems. A community usually refers to a set of nodes that are densely connected among themselves, but sparsely connected to the remaining nodes of the network. Detecting communities has been proved to be a NP-hard problem. Therefore, evolutionary based optimization approaches can be used to solve it. But a primary challenge for them is the higher computational complexity when dealing with large scale networks. In this respect, a COMpression based Multi-Objective Evolutionary Algorithm with Decomposition (Com-MOEA/D) for community detection is proposed where the network is first compressed to a much more smaller scale by exploring network topologies. After that, a framework of multi-objective evolutionary algorithm based on decomposition is applied, in which a local information based genetic operator is proposed to speed up the convergence and improve the accuracy of the Com-MOEA/D algorithm. Experimental results on both real world and synthetic networks show the superiority of the proposed method over several state-of-the-art community detection algorithms.
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