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