Evolutionary Clustering for Mining and Tracking Dynamic Multilayer Networks

This article proposes a framework for community discovery in temporal multiplex networks by extending the evolutionary clustering approach to encompass both time and multiple dimensions. In this extended framework, the problem of finding community structures for time-evolving networks with multiple types of ties is reformulated by adding the concept of dimensional smoothness, relative to a single timestamp, to that of temporal smoothness, at the base of evolutionary clustering. At each timestamp, the method tries to maximize the quality of the clustering obtained for the current multidimensional network and to minimize the differences with respect to that obtained at the previous timestamp. Moreover, the evolution of a community between two consecutive timestamps is maintained by exploiting the Hungarian approach, which determines the best cluster correspondence between two consecutive timestamps. Experiments on synthetic and real-world networks show the capability of the approach in discovering and tracking group organization of actors constituting the network.