Application of a time delay neural network for predicting positive and negative links in social networks
The structure of online social networks cannot in most cases be defined only by the relationships among its members. The interuser relations on the website of social networks are often a mixture of positive and friendly interactions, such as trust and interest, and at the same time negative interactions, such as mistrust and lack of interest. One of the issues with signed social networks is the prediction of edge signs. Some of the most recent studies have tried to extract features of the users and their relations with their neighbors to solve this problem. The results of such works demonstrate a relative success for such efforts. The present paper is an effort to offer a rather new approach to solve the problem that has not been addressed yet. The approach uses the distributed time delay neural network to present a model capable of predicting the sign of hidden or unknown edges. The goal is to have the neural network trained by available data and then assess its efficiency of the network. Our implementation on the actual datasets of Slashdot media shows that the algorithm, while simple, has a higher rate of precision in comparison to other existing methods.