Multiobjective evolutionary community detection for dynamic networks

Abstract

A multiobjective genetic algorithm for detecting communities in dynamic networks, i.e., networks that evolve over time, is proposed. The approach leverages on the concept of evolutionary clustering, assuming that abrupt changes of community structure in short time periods are not desirable. The algorithm correctly detects communities and it is shown to be very competitive w.r.t. some state-of-the-art methods.