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Finding the Optimal Social Trust Path for the Selection of Trustworthy Service Providers in Complex Social Networks

机译:寻找复杂社会网络中选择可信赖服务提供商的最佳社会信任路径

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摘要

Online Social networks have provided the infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers or the recommendation of files as services. In these applications, trust is one of the most important factors in decision making by a service consumer, requiring the evaluation of the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider. However, there are usually many social trust paths between two participants who are unknown to one another. In addition, some social information, such as social relationships between participants and the recommendation roles of participants, has significant influence on trust evaluation but has been neglected in existing studies of online social networks. Furthermore, it is a challenging problem to search the optimal social trust path that can yield the most trustworthy evaluation result and satisfy a service consumer's trust evaluation criteria based on social information. In this paper, we first present a novel complex social network structure incorporating trust, social relationships and recommendation roles, and introduce a new concept, Quality of Trust (QoT), containing the above social information as attributes. We then model the optimal social trust path selection problem with multiple end-to-end QoT constraints as a Multiconstrained Optimal Path (MCOP) selection problem, which is shown to be NP-Complete. To deal with this challenging problem, we propose a novel Multiple Foreseen Path-Based Heuristic algorithm MFPB-HOSTP for the Optimal Social Trust Path selection, where multiple backward local social trust paths (BLPs) are identified and concatenated with one Forward Local Path (FLP), forming multiple foreseen paths. Our strategy could not only help avoid failed feasibility estimation in path selection in certain cases, but also increase the chances of delivering a near-optimal solution with high quality- The results of our experiments conducted on a real data set of online social networks illustrate that MFPB-HOSTP algorithm can efficiently identify the social trust paths with better quality than our previously proposed H_OSTP algorithm that outperforms prior algorithms for the MCOP selection problem.
机译:近年来,在线社交网络为许多新兴应用提供了基础结构,例如,用于服务提供商的推荐或文件作为服务的推荐。在这些应用程序中,信任是服务消费者做出决策的最重要因素之一,需要评估服务提供商沿着从服务消费者到服务提供商的社会信任路径的可信赖性。但是,两个参与者之间通常存在许多彼此不认识的社会信任路径。另外,一些社交信息,例如参与者之间的社交关系和参与者的推荐角色,对信任评估有重大影响,但在现有的在线社交网络研究中却被忽略。此外,寻找能够产生最值得信赖的评估结果并满足基于社会信息的服务使用者的信任评估标准的最佳社会信任路径是一个具有挑战性的问题。在本文中,我们首先提出了一个包含信任,社交关系和推荐角色的新颖复杂的社交网络结构,并引入了包含上述社交信息作为属性的新概念“信任质量(QoT)”。然后,我们将具有多个端到端QoT约束的最优社会信任路径选择问题建模为多约束最优路径(MCOP)选择问题,该问题被证明是NP完全的。为了解决这一难题,我们提出了一种新颖的基于多路径的启发式算法MFPB-HOSTP,用于最优社会信任路径的选择,其中识别了多个反向本地社会信任路径(BLP),并将其与一个正向本地路径(FLP)串联在一起。 ),形成多个可预见的路径。我们的策略不仅可以帮助避免在某些情况下进行路径选择时的可行性估计失败,而且还可以增加提供高质量的近最优解决方案的机会-我们在在线社交网络的真实数据集上进行的实验结果表明,与我们先前提出的H_OSTP算法相比,MFPB-HOSTP算法可以有效地识别质量更高的社会信任路径,该算法优于针对MCOP选择问题的现有算法。

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