Uncertainty-preserving trust prediction in social networks

Abstract

The trust metric has became an increasingly important element of social networks. While collecting, processing and sharing information is becoming easier and easier, the problem of the quality and reliability of that information remains a significant one. The existence of a trust network and methods of predicting trust between users who do not know each other are intended to help in forming opinions about how much to trust information from distant sources. In this chapter we discuss some basic concepts like trust modeling, trust propagation and trust aggregation. We briefly recall recent developments in the area and present a new approach that focuses on the uncertainty aspect of trust value. To this end we utilize a theory of incompletely known fuzzy sets and we introduce a new, uncertainty-preserving trust prediction operator based on group opinion and on relative scalar cardinality of incompletely known fuzzy sets. Motivated by the need for proper uncertainty processing, we have constructed a new method of calculating relative scalar cardinality of incompletely known fuzzy sets that ensures the monotonicity of uncertainty. We outline the problem of uncertainty propagation, and we illustrate by examples that the proposed operator provides most of the desirable properties of trust and uncertainty propagation and aggregation. © 2014 Springer International Publishing Switzerland.