TREEXTRUST: TOPIC-AWARE COMPUTATIONAL TRUST BASED ON INTERACTION EXPERIENCE, REPUTATION OF USERS WITH SIMILARITY AND PATH ALGEBRA OF GRAPH IN SOCIAL NETWORKS

Trust measure is confidence or reliability among users or peers and has been studied widely in online social networks. Most trust models currently are based on the concepts of interaction trust and reputation trust. However, various forms of interaction and analysis of interaction contexts have been not considered fully for trust estimation. Moreover, the mechanism for computing reputation trust based on propagation lacks a clear foundation and is expensive in computation. The purpose of this paper is to present a family of models of computational trust, named TreeXTrust, for estimating a trust degree of a user truster on another user trustee. Our model is a mathematical formulation based on aggregation of the topic-aware experience trust with various forms of interaction and the topic-aware reputation trust with users’ similarity and operators on path algebra in graph. We conduct experiments to evaluate how impact of interaction forms and users’ interests on experience trust and the correlation of experience trust and reputation trust on overall trust estimation. Our experimental results have demonstrated that: (i) Interest degrees influence on experience trust more than interaction ones; (ii) Community evaluation of some trustee affects the overall trust estimation more than the truster’s individual evaluation. Our family of models outperforms the state of art methods presented in the literature and is a framework for selecting and implementing a suitable model of computational trust for our problem at hand.