Uncovering communities in multidimensional networks with multiobjective genetic algorithms

A framework for community discovery in multidimensional networks based on an evolutionary approach is proposed. Each network is clustered by running a multiobjective genetic algorithm that tries to maximize the modularity function of the current network and, at the same time, to minimize the difference between the current community structure and that obtained on the already considered dimensions. Experiments on synthetic datasets show the capability of the approach in discovering latent shared group organization of individuals.