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

TL;DR

This paper introduces a scalable framework with four algorithms for accurately computing bias and prestige in trust networks, enhancing trustworthiness and importance assessments in social networks.

Contribution

The paper proposes a novel framework using vector-valued contractive functions to measure bias, along with four scalable algorithms for computing bias and prestige in trust networks.

Findings

Algorithms are effective and robust.

Time and space complexities are linear.

Validated on five real datasets.

Abstract

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.