Evolution of trust networks in social web applications using supervised learning

Abstract

Trust as a major part of human interactions plays an important role in addressing information overload, and helping users collect reliable information in SocialWeb applications. Although many researchers have already conducted comprehensive studies on the trust related online applications, the understanding of trust evolution is still unclear to the researchers. In this study, we move toward time-aware trust prediction in evolving online trust networks. Achieving this, we investigate the impact of considering the temporal evolution of trust networks explicitly in trust prediction tasks by using a supervised learning method. We incorporate the history information available on the trust relations (or links) of the current trust network state in prediction process. Our results unequivocally show that timestamps of past trust relations significantly improve the prediction accuracy of future trust relations.