A cooperative evolutionary approach to learn communities in multilayer networks

In real-world complex systems objects are often involved in different kinds of connections, each expressing a different aspect of object activity. Multilayer networks, where each layer represents a type of relationship between a set of nodes, constitute a valid formalism to model such systems. In this paper a new approach based on Genetic Algorithms to detect community structure in multilayer networks is proposed. The method introduces an extension of the modularity concept and adopts a genetic representation of a multilayer network that allows cooperation and co-evolution of individuals, in order to find an optimal division of the network, shared among all the layers. Moreover, the algorithm relies on a label propagation mechanism and a local search strategy to refine the result quality. Experiments show the capability of the approach to obtain accurate community structures.