Extended structural balance theory for modeling trust in social networks

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 and the relationships vary in their level of strength. In this paper, we introduce a new extended balance theory as a foundational theory of trust in networks. Our theory preserves the distinctions between trust and distrust as suggested in the literature, but also incorporates the notion of relationship strength which 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 experiments on real datasets.