PIN-TRUST: Fast trust propagation exploiting positive, implicit, and negative information

Abstract

Given "who-trusts/distrusts-whom" information, how can we propagate the trust and distrust? With the appearance of fraudsters in social network sites, the importance of trust prediction has increased. Most such methods use only explicit and implicit trust information (e.g., if Smith likes several of Johnson's reviews, then Smith implicitly trusts Johnson), but they do not consider distrust. In this paper, we propose PIN-TRUST, a novel method to handle all three types of interaction information: explicit trust, implicit trust, and explicit distrust. The novelties of our method are the following: (a) it is carefully designed, to take into account positive, implicit, and negative information, (b) it is scalable (i.e., linear on the input size), (c) most importantly, it is effective and accurate. Our extensive experiments with a real dataset, Epinions.com data, of 100K nodes and 1M edges, confirm that PIN-TRUST is scalable and outperforms existing methods in terms of prediction accuracy, achieving up to 50.4 percentage relative improvement.