Trust Evaluation in Online Social Networks Based on Knowledge Graph

With the development of Online Social Networks(OSNs), OSNs have been becoming very popular platforms to publish resources and to establish relationship with friends. However, due to the lack of prior knowledge of others, there are usually risks associated with conducting network activities, especially those involving money. Therefore, it will be necessary to quantify the trust relationship of users in OSNs, which can help users decide whether they can trust another user. In this paper, we present a novel method for evaluating trust in OSNs using knowledge graph (KG), which is the cornerstone of artificial intelligence. And we focus on the two contributions for trust evaluation in OSNs: (i) a novel method using RNN to quantify trustworthiness in OSNs, which is inspired by relationship prediction in KG; (ii) a Path Reliability Measuring algorithm (PRM) to decide the reliability of a path from the trustor to the trustee. The experiment result shows that our method is more effective than traditional methods.