A Regularized Optimization Model for Community Identification in Complex Networks

Identifying community structure is an important step to reveal the functional characteristics of complex networks. Recently, many models and algorithms have been designed to identify communities in a given network. Here, we propose a general mathematical programming framework to optimize some modularity criteria under certain constraints. We then show that several existing models are special cases of our framework by taking different kinds of modularity criteria and constraints. In addition, a regularization term is introduced as an additional objective to consider the parsimony principle in community structure. Experiments on several toy networks show that our new model is simple yet insightful for the community identification problem.

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