Link prediction via a neighborhood-based nonnegative matrix factorization model

Abstract

Link prediction is an important issue to understand the dynamics and evolution mechanisms of complex networks. Traditional link prediction algorithms are based on the topological properties of the underlying network in terms of graph theory. In order to improve the accuracy of link prediction, recent researches increasingly focus on modeling the link behaviors from the latent structure information of the networks. In this paper, we propose a neighborhood-based nonnegative matrix factorization model to solve the problem of link prediction. Our model learns latent feature factors from the overall topological structure combing with local neighborhood structures of the underlying network. Extensive experiments on real-world networks demonstrate the effectiveness and efficiency of our proposed model.