Computational and Applied Mathematics

Overlapping community detection in complex networks using multi-objective evolutionary algorithm

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Community structure is an important topological property of complex networks, which has great significance for understanding the function and organization of networks. Generally, community detection can be formulated as a single-objective or multi-objective optimization problem. Most existing optimization-based community detection algorithms are only applicable to disjoint community structure. However, it has been shown that in most real-world networks, a node may belong to multiple communities implying overlapping community structure. In this paper, we propose a multi-objective evolutionary algorithm for identifying overlapping community structure in complex networks based on the framework of non-dominated sorting genetic algorithm. Two negatively correlated evaluation metrics of community structure, termed as negative fitness sum and unfitness sum, are adopted as the optimization objectives. In our algorithm, link-based adjacency representation of overlapping community structure and a population initialization method based on local expansion are proposed. Extensive experimental results on both synthetic and real-world networks demonstrate that the proposed algorithm is effective and promising in detecting overlapping community structure in complex networks.


28 Jul 2015


Computational and Applied Mathematics