Multivalued Decision Diagrams-Based Trust Level Analysis for Social Networks

Interactions among different parties within social networks are greatly dependent on trust. Therefore, trust analysis is significant for solving social network related problems such as privacy protect, and rumor tracking and containment. This paper makes advancements in the trust analysis by proposing a reliability model-based algorithm for assessing the trust level of any two parties within a social network. Particularly, a multi-level trust model with the probability distribution is proposed and a multivalued decision diagrams (MDD)-based method is suggested for assessing the trust level of two parties that may be connected through multiple indirect or direct links. These connection paths may be correlated due to sharing a common party or link. Further, the MDD-based method is extended for performing a trust sensitivity analysis with the aim to pinpoint which direct link contributes the most to the trust relationship between two non-directly connected parties within the social network. Dynamics in trust are also investigated. Examples are provided to illustrate the proposed probabilistic MDD-based method for trust and sensitivity analyses. Performance of the proposed method is evaluated through experiments and comparisons with existing trust evaluation methods.