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.

社交网络的快速发展促进了信息交换，而在这些生态系统中，恶意行为也在稳步增长。这导致个人难以信任其他方。本文研究信任关系链中信任的传播，以计算现有用户的信任值。在这项研究中，介绍了一种精确选择可信赖路径以及基于最可靠路径的间接信任值集成的方法。所提出的方法融合了A *算法和使用多准则决策方法（即TOPSIS方法）在模糊环境下寻找最可靠路径的思想。此外，为了选择最合适的中间节点，需要使用一组准则，例如拓扑相似性，轮廓相似性，邓巴定理，局部信任和上下文信任被考虑。所提出方法的评估结果证明了在不同平均路径长度下传播的信任距离，同时保留了每个未连接节点对之间的推断信任值的准确性。使用具有不同拓扑结构的Facebook和Twitter网络执行评估，并将结果与​​TidalTrust和MoleTrust算法进行比较。