Abstract
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.
Highlights
As a conception in social science, a social network uses actors and relations in the form of a graph model to indicate relationships or interactions between different actors [1]
We propose a combinatorial method based on multivalued decision diagrams (MDDs) for determining the probability distribution of different trust levels between two parties, which may be connected through a direct link and/or
A probabilistic combinatorial method based on MDDs is proposed to determine the probability distribution of different trust levels between any two parties within the social network, which may be connected through multiple paths involving a direct link or multiple indirect links
Summary
As a conception in social science, a social network uses actors and relations in the form of a graph model to indicate relationships or interactions between different actors [1]. To address the above described limitations of the existing trust representation and analysis models, we extend the easy-to-use multi-level DLM by considering a probability distribution for different levels of a direct trust relationship. We propose a combinatorial method based on multivalued decision diagrams (MDDs) for determining the probability distribution of different trust levels between two parties, which may be connected through a direct link and/or. The proposed MDD-based method can address the asymmetric, propagative and composable properties of social networks while addressing the dependencies of different connection paths automatically during the MDD model generation. The remainder of the paper is organized as follows: Section II presents related work on trust modeling and evaluation for OSNs. Section III describes basics of MDDs. Section IV presents the proposed MDD-based method for the two-party trust level evaluation and related sensitivity analysis in the social network.
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