Strup: Stress-Based Trust Prediction in Weighted Sign Networks

Abstract

Trust/distrust networks in social media are called weighted sign networks, in which edges are labeled with real numbers. An algorithm is proposed in this paper to improve trust prediction in WSNs by using local variables. Our algorithm, Strup, predicts the sign of edges through computing the stress of related nodes. Four new parameters are introduced to demonstrate the stress of nodes in the networks and to predict the sign of edges accurately. Considering these signs leads to more precise trust prediction. The experiment on four real-world WSNs showed that our proposed approach can easily combine with most of the existing weight prediction algorithms to improve them. Due to the tight relation between trust prediction and (opinion and emotion) prediction, we believe that our stress-based algorithm could be a promising solution in their challenging domains.

Link Prediction Model for Weighted Networks Based on Evidence Theory and the Influence of Common Neighbours

“…However, with network scale expansion, its computational complexity increased. Naderi P T et al [31] constructed an algorithm to improve trust prediction in weighted signed networks by using local variables. However, the method focuses on the prediction of the sign of edges, and there is not much research on the role of the weight in link prediction in weighted networks.…”