Distrust Prediction in Signed Social Network

In social networks, the most studies focus on the trust prediction, but distrust cannot get enough attention. The distinct characteristics of distrust relations present challenges to traditional relation prediction. Distrust relations are very sparse in social network, and negative interaction data is too little. We embark on the problem to investigate the distrust prediction with only network topology. After achieving seven social distrusting-inducing factors, we adopt machine learning and optimization methods to model the prediction process. The framework of Distrust prediction in Signed social network (DP-SSN) is proposed, which can predict distrust relations without any interaction data. Empirically, we perform extensive experiments on real-world data to corroborate the effectiveness of the proposed framework.