TY - JOUR

T1 - A Framework of Algorithms

T2 - Computing the Bias and Prestige of Nodes in Trust Networks

AU - Li, Rong Hua

AU - Yu, Jeffrey Xu

AU - Huang, Xin

AU - Cheng, Hong

N1 - Funding information:
The work was supported by grant of the Research Grants Council of the Hong Kong SAR, China Nos. 419109 and 411211. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher copyright:
© 2012 Li et al.

PY - 2012/12/11

Y1 - 2012/12/11

N2 - 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 with respect to 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.

AB - 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 with respect to 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.

UR - http://www.scopus.com/inward/record.url?scp=84871165708&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0050843

DO - 10.1371/journal.pone.0050843

M3 - Article

C2 - 23239990

AN - SCOPUS:84871165708

SN - 1932-6203

VL - 7

JO - PLoS ONE

JF - PLoS ONE

IS - 12

M1 - e50843

ER -