Foundations of Trust and Distrust in Networks

Abstract

Modeling trust in very large social networks is a hard problem due to the highly noisy nature of these networks that span trust relationships from many different contexts, based on judgments of reliability, dependability, and competence. Furthermore, relationships in these networks vary in their level of strength. In this article, we introduce a novel extension of structural balance theory as a foundational theory of trust and distrust in networks. Our theory preserves the distinctions between trust and distrust as suggested in the literature, but also incorporates the notion of relationship strength that can be expressed as either discrete categorical values, as pairwise comparisons, or as metric distances. Our model is novel, has sound social and psychological basis, and captures the classical balance theory as a special case. We then propose a convergence model, describing how an imbalanced network evolves towards new balance, and formulate the convergence problem of a social network as a Metric Multidimensional Scaling (MDS) optimization problem. Finally, we show how the convergence model can be used to predict edge signs in social networks and justify our theory through extensive experiments on real datasets.