Evolutionary community mining for link prediction in dynamic networks

Abstract

Dynamic networks are inherently evolutionary in nature where the characteristics, behaviour and activities of their constituents (i.e., actors and links) change temporally. In regards to time-evolving model in social network analyses, link prediction supports the understanding of the rationale behind the underlying growth mechanisms of social networks. Mining the temporal patterns of actor-level evolutionary changes in regards to their network neighbourhood, and community-based dynamic information may suitably be used for the purpose of dynamic link prediction. Considering the evolutionary aspects of mesoscale network structure (i.e., community), this study sought to build dynamic similarity metrics or dynamic features to measure similarity/proximity between actor-pairs. A supervised link prediction model was set out where these features were employed as instance features and performances were compared against an existing topological similarity-based link prediction metric. High-performance scores achieved by these dynamic feature, examined in this study, represent them as prospective candidates not only for dynamic link prediction task but also understanding of underlying network evolution mechanism.