Particle Competition for Multilayer Network Community Detection

Multilayer complex networks are suitable models to represent high-dimensional heterogeneous systems with special importance in big data era. Community structures in a multilayer network can be drastically changed in comparison to the set of isolated monolayer networks composited by the same sets of nodes due to the existence of interlayer connections. For this reason, community detection in multilayer networks, as an unsupervised learning task, has turned out to be an interesting research topic in data mining and analysis in complex systems. In this paper, we propose a modified version of the particle competition model for multilayer network community detection. The original model was designed to community detection in monolayer unweighted and undirected networks. The modified version presented in this paper can be in turn applied to multilayer, weighted, and/or directed networks. Moreover, we also propose a localized measure to determine the optimal number of particles corresponding to the correct number of detected communities. Computer simulations shows the better performance of the proposed technique over the state of the art ones.