An Adaptive Neuro-fuzzy Inference System to Evaluate Trustworthiness of Users in a Social Network

In recent years, the emergence of various social networks has led to the growth of s ocial network users. However, activity in such networks depends on the level of trust that users hav e in each other. Therefore, trust is essential and important issue in these networks, especially whe n users interact with each other. In this article, we examine this issue and provide a method to evaluate it.It is not easy to measure the accuracy of trust for users who interact with social networks. Here,interactions are virtual. In this article, we have used the adaptive neuro-fuzz inference system to evaluate trustworthiness by considering different personality attributes of users such as reli ability,availability, interest, patience and adaptability. Using these features as input and based on the adaptive neuro-fuzzy inference system,we evaluated the trustworthiness of users in socialnetwork.Thep roposed adaptive neuro-fuzzy inference system is expandable because in this system,trustcan be defin ed as a set of one or more personality attributes.Epinions social network dataset is also used to sim ulate and validate the proposed method. In the proposed method, the absolute mean value of error is less than 0.0095 and the value of F-score is more than 0.9884. Based on theobtainedresults and com pared to the previous methods,the proposed adaptive neuro-fuzzy inference systemshows an acceptable accuracy for evaluating the trustworthiness of users.