An efficient multiple trust paths finding algorithm for trustworthy service provider selection in real-time Online Social Network environments

Online Social Networks (OSNs) have been used to enhance service provision and service selection, where trust is one of the most important factors for the decision making of service consumers. Thus, it is significant to evaluate the trustworthiness of the service providers along the social trust paths from a service consumer to a service provider. However, there are usually many social trust paths between an unknown service consumer and service provider. Thus, a challenging problem is how to effectively and effciently find those social trust paths that can yield trustworthy trust evaluation results based on the requirements of a service consumer particularly in the realtime OSN environments. In this paper, we first present a contextual trust-oriented social network structure and a concept of Quality of Trust (QoT). We then model the multiple social trust paths finding with end-to-end QoT constraints as the Multiple Constrained K Optimal Paths (MCOP-K) selection problem, which is NP-Complete. To deal with this challenging problem, based on the Monte Carlo method and our optimization search strategies, we propose a new efficient and effective approximation algorithm D-MCBA. The results of our experiments conducted on a real-world dataset of OSNs illustrate that D-MCBA can efficiently identify the social trust paths with better quality than our previously proposed MONTE-K algorithm that is the most promising algorithm for the social trust path finding in OSNs.