Unsupervised Negative Link Prediction in Signed Social Networks

Abstract

It has been proved in a number of applications that it is useful to predict unknown social links, and link prediction has played an important role in sociological study. Although there has been a surge of pertinent approaches to link prediction, most of them focus on positive link prediction while giving few attentions to the problem of inferring unknown negative links. The inherent characteristics of negative relations present great challenges to traditional link prediction: (1) there are very few negative interaction data; (2) negative links are much sparser than positive links; (3) social data is often noisy, incomplete, and fast-evolved. This paper intends to address this novel problem by solely leveraging structural information and further proposes the UN-PNMF framework based on the projective nonnegative matrix factorization, so as to incorporate network embedding and user’s property embedding into negative link prediction. Empirical experiments on real-world datasets corroborate their effectiveness.