by the immense growth of social applications in web environment, the role of trust in connecting people is getting more important than ever. Although many researchers have already conducted comprehensive studies on the trust related applications, the understanding of distrust relations is still unclear to the researchers. In this paper, we have investigated some of mechanisms that determine the signs of links in trust networks which consist of both trust and distrust relationships. Achieving this, we develop a framework of trust sign prediction, taking a machine-learning approach. We report experiments conducted on Epinions which is a well-known and very large collection of data dealing with trust computation. Empirical results show that the sign of relations in the trust networks can be effectively predicted using pre-trained classifiers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.