Detection of trust links on social networks using dynamic features

Social networks have turned into a popular medium for diffusion of information, providing connections between people through access to several networks and shared personal opinions, thoughts, information, and experiences. A web-based social network consists of users who are increasingly concerned with protection of their privacy, which is considered as an important concern about user privacy due to the nature of social networks and privacy protection. Therefore, every network user prefers to identify people trusted by him and to communicate with them in order not to be abused by untrusted people. In this paper, we seek to predict trust links by utilizing the most important features of each user on a social network, so that the users can pursue their everyday purposes without much concern. In order to provide trust in social networks, we made use of several features of the users at the same time, the most important of which include the extent to which they trust each other, the amount of similarity between their trust, and each one’s reputation. Using these measures, a trust path was created on the network for each connected user, compared with four common methods for verification of its truth. The results demonstrate that the proposed method has been capable of outperforming the Katz, h Trust, TP, and RS methods in terms of effectiveness, efficiency, and strength, and can more accurately present a more reliable path within an acceptable runtime.