A trust evaluation model for online social networks

Abstract

With the rapid prevalence of online social networks, the trustworthiness of online users has become a current issue in the field of social computing. The evaluation of trust in social networks has been widely used in situations such as friend-recommendation, e-commerce and trust based access control systems. Security is the backbone of social networks. For sharing and exchanging of information between the trusted users only trustworthiness of the user needs to be determined. One of the key requirements in trust applications is recognizing the trustworthy actors in the network. In the proposed research, a general trust framework will be introduced to calculate the node trust values for social network users by applying reinforcement learning methods. Firstly, some selected features of social network are used as the training feature and the measurement whether there is an edge between nodes used as label information. Secondly, a training model will be used to calculate the node trust value. Then a recommendation algorithm will be used to calculate node trust score. Finally, the simulation is used to verify the performance of suggested method. For the simulation of experimentation, data from an adaptive social network will be used.