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A trust network is a social network in which edges represent the trustrelationship between two nodes in the network. In a trust network, afundamental question is how to assess and compute the bias and prestige of thenodes, where the bias of a node measures the trustworthiness of a node and theprestige of a node measures the importance of the node. The larger bias of anode implies the lower trustworthiness of the node, and the larger prestige ofa node implies the higher importance of the node. In this paper, we define avector-valued contractive function to characterize the bias vector whichresults in a rich family of bias measurements, and we propose a framework ofalgorithms for computing the bias and prestige of nodes in trust networks.Based on our framework, we develop four algorithms that can calculate the biasand prestige of nodes effectively and robustly. The time and space complexitiesof all our algorithms are linear w.r.t. the size of the graph, thus ouralgorithms are scalable to handle large datasets. We evaluate our algorithmsusing five real datasets. The experimental results demonstrate theeffectiveness, robustness, and scalability of our algorithms.

A Framework of Algorithms: Computing the Bias and Prestige of Nodes in Trust Networks