A very fast and robust trust inference algorithm in weighted signed social networks using controversy, eclecticism, and reciprocity

Abstract

The importance of trust in social networks instigated many research efforts to understand it and predict it. However, the complex nature of trust and its counterpart; distrust; makes these tasks challenging. While early trust inference approaches ignore distrust, it seems that this concept gained much attention in recent years. Surely, knowing whom to distrust is as important as knowing whom to trust. We show in this paper that trust and distrust can be quickly predicted using some social traits of the trustor and the trustee. Using a "tug of war" analogy involving these traits, we have devised an intuitive approach that uses only the direct neighbors of the trustor and those of the trustee to predict both trust and distrust. Experiments on four real-world social networks show that our algorithm is very fast, provides good predictions, and is robust to network sparsity.

Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation

“…In [16] authors present semiring based trust aggregation method that handles both trust and distrust to infer trust for trust-based recommender systems. Akilal et al [17] present a fast and robust trust computation method based on controversy, eclecticism, and reciprocity that handles both trust and distrust. They use trusting and being trusted pattern of trustor and trustee to compute trust from trustor to trustee.…”

“…The difference of positive and negative sentiment score is used as the weight which lies in interval [−1,1]. The dataset has been used to evaluate fairness and goodness [15] and TOW [17] and is available at [19].…”

“…Reference [17] Describe three characteristics of nodes i.e. controversy, eclecticism, and reciprocity.…”

“…The first experiment performed was leave one out. This is experiment is commonly used to evaluate the accuracy of continuous trust prediction and edge weight prediction algorithms [15], [17]. The accuracy of predicted trust value for a given pair of users is compared to the actual weight of edge in the network.…”

“…Recently with availability of more datasets with both trust and distrust information, a few algorithms [15]- [17] have been developed that estimate both trust and distrust as a continuous value. However, some of these algorithms are not efficient to be applied on very large networks or are not based on trust propagation.…”