Trust Prediction via Belief Propagation

Abstract

The prediction of trust relationships in social networks plays an important role in the analytics of the networks. Although various link prediction algorithms for general networks may be adapted for this purpose, the recent notion of “trust propagation” has been shown to effectively capture the trust-formation mechanisms and resulted in an effective prediction algorithm. This article builds on the concept of trust propagation and presents a probabilistic trust propagation model. Our model exploits the modern framework of probabilistic graphical models, more specifically, factor graphs. Under this model, the trust prediction problem can be formulated as a statistical inference problem and we derive the belief propagation algorithm as a solver for trust prediction. The model and algorithm are tested using datasets from Epinions and Ciao, by which performance advantages over the previous algorithms are demonstrated.