Mesoscopic analysis of networks with genetic algorithms

The detection of communities is an important problem, intensively investigated in recent years, to uncover the complex interconnections hidden in networks. In this paper a genetic based approach to discover communities in networks is proposed. The algorithm optimizes a simple but efficacious fitness function able to identify densely connected groups of nodes with sparse connections between groups. The method is efficient because the variation operators are modified to take into consideration only the actual correlations among the nodes, thus sensibly reducing the search space of possible solutions. Experiments on synthetic and real life networks show the ability of the method to successfully detect the network structure.