title: Social Trust Prediction Using Heterogeneous Networks.
abstract: Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.
authors.affiliation: University of Texas at Arlington.
authors.forename: Jin
authors.initials: J
authors.lastname: Huang
authors.affiliation: University of Texas at Arlington.
authors.forename: Feiping
authors.initials: F
authors.lastname: Nie
authors.affiliation: University of Texas at Arlington.
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.affiliation: University of South Florida.
authors.forename: Yi-Cheng
authors.initials: YC
authors.lastname: Tu
authors.affiliation: University of Texas at Arlington.
authors.forename: Yu
authors.initials: Y
authors.lastname: Lei
keywords: Algorithms; Experimentation; Trust prediction; nonnegative matrix factorization; social network; transfer learning
references.citation: Science. 2000 Dec 22;290(5500):2323-6
references.pmid: 11125150
references.citation: IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):45-55
references.pmid: 19926898