Community-based link prediction

Abstract

This work proposes a community-based link prediction approach for identifying missing links or the links that are likely to appear in near future. Earlier works on link prediction consider only connectivity pattern or node attributes. We incorporate the notion of community structure in link prediction. An algorithm is designed to account the influence of communities on link prediction. We have considered recently developed edge centrality measures to compute likelihood scores of missing links. The performance of proposed algorithm is analyzed in terms of three metrics and execution time on both real-world networks and synthetic networks, where ground truth communities are already defined. The time complexity of proposed algorithm is also analyzed. © 2017, Springer Science+Business Media New York.