A propagation trust model in social networks based on the A* algorithm and multi-criteria decision making

The rapid growth of social networks facilitates the exchange of information whereas malicious behaviors are also steadily increasing in these ecosystems. This results in a challenging situation for individuals to trust other parties. This paper studies the propagation of trust within a chain of trust relations to calculate the trust values of existing users. In this research, an approach for the precise selection of trustworthiness paths as well as the integration of indirect trust values based on the most reliable routes is introduced. The presented approach fuses the ideas from the A* algorithm and multi-criteria decision making approaches using (i.e. TOPSIS method) under fuzzy environments for finding the most reliable path. Moreover, for selecting the most appropriate middle node, a set of criteria such as topological similarity, profile similarity, Dunbar's theorem, local trust, and contextual trust are considered. The evaluation results of the proposed approach demonstrate the propagated trust distance with the different average path lengths while preserving the accuracy of the inferred trust values between each unconnected pair of nodes. The evaluations are performed using the Facebook and Twitter networks having different topologies and the results are compared to the TidalTrust and the MoleTrust algorithms.