Detecting communitystructure is crucial for uncovering the links between structures and functions in complex networks. Most contemporary communitydetectionalgorithms employ single optimization criteria (e. g., modu...
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ISBN:
(纸本)9781424481262
Detecting communitystructure is crucial for uncovering the links between structures and functions in complex networks. Most contemporary communitydetectionalgorithms employ single optimization criteria (e. g., modularity), which may have fundamental disadvantages. This paper considers the communitydetection process as a Multi-Objective optimization Problem (MOP). Correspondingly, a special Multi-Objective Evolutionary algorithm (MOEA) is designed to solve the MOP and two model selection methods are proposed. The experiments in artificial and real networks show that the multi-objective communitydetectionalgorithm is able to discover more accurate communitystructures.
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