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A method for computing trust value in mobile social networks with fuzzy approach
Abstract
Large-scale mobile social networks (MSNs) facilitate connections through mobile devices, and users of these networks can use mobile to access, share and distribute information. With the increasing the number of users on social networks, the large amount of shared information and the dissemination of information has created challenges for users. One of these challenges is the trust of users to each other. Trust can play an important role in the users' decision-making in social networks, so that most people share their information based on their trust to others, or make decisions relying on information provided by users. 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.
Keywords: Fuzzy classification, Fuzzy logic, Mobile social networks, Trust, Trust computing model
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