Trust Prediction in Multiplex Networks

the proliferation of social networks and their popularity among web users has lead a lot of researches on their analysis. One of the social networks classes is trust networks in which the links indicates trust or distrust. There are some challenges for these networks, challenges like interaction with anonymous users, inaccurate equations, time sensitivity and etc. Due to lots of users, the first challenge has a high level of importance and one of its solutions is predicting trust and distrust values. In this manuscript we proposed a new method for trust and distrust prediction. To implement our method, first of all we constructed a multiplex network for our problem which consists of two layers and the relations in each layer have different concepts; one layer indicates trust relations and the other indicates similarity relations. We then ranked this multiplex network's nodes by using degree ranking method for this kind of networks and used these ranks to obtain optimism and reputation, which constructs our feature vectors for a logistic regression predictor. Our experiments on Opinions real data set, showed that accuracy of our proposed method in predicting trust and distrust relations is higher than the previous methods including the cluster-based collaborative filtering one those based on social and balance theory, and one based on a machine learning framework.

Keywords:

social networks , trust prediction , multiplex networks , ranking nodes