Trust Network Visualization Based on Force-Directed Layout

TL;DR: The public influence weight, the trust strength and the trust distance are defined in this paper and the force-directed visualization algorithm for trust network is proposed and the experimental results show that it can reveal the nature of communities better.

Abstract: As a formal description approach, the trust network can intuitively describe trust relationships among entities in virtual communities and attracts great attentions. The trust network visualization is a key to explore the mechanism of trust propagation and study the trust evaluation. Most existing network visualization approaches only take into account the undirected and unweighted graphs, which cannot illustrate the asymmetry and the discreteness of trust relationships. To solve this issue, we define the public influence weight, the trust strength and the trust distance in this paper and propose the force-directed visualization algorithm for trust network. In the visualization algorithm, the desirable length between two nodes is determined by the trust strength so as to cluster trusted entities, and the forces between two different nodes are determined by their public influence weights so as to center on the heavyweight entities. Finally, the visualization experiments are carried out on the Epinions data set and the performance of the proposed algorithm is compared with the classical force-directed layout algorithm. The experimental results show that it can reveal the nature of communities better.