Incorporating evidence into trust propagation models using Markov Random Fields

H. Tosun, John W. Sheppard
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引用次数: 4

Abstract

Current trust models for social networks commonly rely on explicit voting mechanisms where individuals vote for each other as a form of trust statement. However, there is a wealth of information about individuals beyond trust voting in emerging web based social networks. Incorporating sources of evidence into trust models for social networks has not been studied to date. We explore a trust model for social networks based on Markov Random Fields, which we call MRFTrust, that allows us to incorporate sources of evidence. To allow comparative evaluation, a state-of-the-art local trust algorithm—MoleTrust—is also investigated. Experimental results of the algorithms reveal that our trust algorithm that incorporates evidence performs better in terms of coverage. It is competitive with the MoleTrust algorithm in prediction accuracy and superior when focusing on controversial users.
利用马尔可夫随机场将证据纳入信任传播模型
当前的社交网络信任模型通常依赖于明确的投票机制,其中个人相互投票作为信任声明的一种形式。然而,在新兴的基于网络的社交网络中,除了信任投票之外,还有大量关于个人的信息。将证据来源纳入社会网络的信任模型至今还没有研究。我们探索了一种基于马尔科夫随机场的社交网络信任模型,我们称之为MRFTrust,它允许我们合并证据来源。为了进行比较评估,还研究了一种最先进的本地信任算法——moletrust。算法的实验结果表明,我们的纳入证据的信任算法在覆盖方面表现更好。它与MoleTrust算法在预测精度上具有竞争力,在关注有争议的用户时具有优势。
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