A new approach for trust computing in mobile social networks

Mobile social networks facilitate connections through mobile devices, and users of these networks can use mobile to access, share and distribute information. With increasing the number of users on social networks, the large amount of shared information and the dissemination of created information cause some problems such as rumor propagation and access to incorrect information. The most powerful tools for validity of received information is based upon the trust value which is assigned by the others. However, considering the subjective and perceptive nature of the concept of trust, the mapping of trust in a computational model is one of the important issues in computing systems of social networks. In addition, there may be various communities on social networks and all users will not be directly connected to each other, which leads to a more complex process of calculating trust. In this research, using user characteristics in social networks, a fuzzy classification approach is proposed, and the trust is computed between users in a class using a computational model. Also trust is gained between users who are not directly connected, using the combination, transition and aggregation processes. By comparing the results, it can be seen that the proposed method recognizes trustworthy people with high precision.