Trust assessment in online social networks
MetadataShow full item record
Assessing trust in online social networks (OSNs) is critical for many applications such as online marketing and network security. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. To address these challenges, we model trust by proposing the three-valued subjective logic (3VSL) model. 3VSL properly models the uncertainties that exist in trust, thus is able to compute trust in arbitrary graphs. We theoretically prove the capability of 3VSL based on the Dirichlet-Categorical (DC) distribution and its correctness in arbitrary OSN topologies. Based on the 3VSL model, we further design the AssessTrust (AT) algorithm to accurately compute the trust between any two users connected in an OSN. AT is able to accurately conduct one-to-one trustworthiness, however, it is inefficient in addressing the massive trust assessment (MTA) problem, i.e., computing one-to-many trustworthiness in OSNs. MTA plays a vital role in OSNs, e.g., identifying trustworthy opinions in a crowdsourcing system. If the AssessTrust algorithm is applied directly to solve the MTA problem, its time complexity is exponential. To efficiently address MTA, we propose the OpinionWalk algorithm that yields an polynomial-time complexity. OpinionWalk uses a matrix to represent a social network's topology and a vector to store the trustworthiness of all users in the network. The vector is iteratively updated when the algorithm 'walks' through the entire network. To validate the 3VSL model, we first conduct a numerical analysis. An online survey system is then implemented to validate the correctness and accuracy of 3VSL in the real world. Finally, we validate 3VSL against two real-world OSN datasets: Advogato and Pretty Good Privacy (PGP). Experimental results indicate that 3VSL can accurately model the trust between any pair of indirectly connected users in the Advogato and PGP. To evaluate the performance of the AssessTrust and OpinionWalk algorithms, we use the same datasets. Compared to the state-of-art solutions, e.g., EigenTrust and MoleTrust, OpinionWalk yields the same order of time complexity and a higher accuracy in trust assessment.