基于信息源聚类的最大熵加权信任分析算法

摘要

在信息网络中，不同的信息源以不同的可信性和准确性提供了各式各样的信息。为了预测这些信息反映事实的真实度，学者们提出了一些信任分析算法来迭代地计算信息源的信任度及其提供事实的准确度。然而这些算法往往忽略了信息源和事实描述对象之间的相关性。本文作者提出了一种基于信息源聚类的最大熵加权信任分析算法，该算法使我们能够在进行信任分析时有效地融合诸如描述对象属性、信息源关联性等信息。实验证明该算法能够明显的提高分析性能。%In information network ,different sources publish facts with different degrees of credibility and accuracy .To predict the truth values of the facts ,several fact-finder algorithms are suggested which iteratively compute the trustworthiness of an informa-tion sourceand the accuracy of the facts it provides .However they ignore a great deal of relevant background and contextual infor-mation .In this paper ,we proposed a novel maximum entropy weighted method to processing trust analysis ,allowing us to elegantly incorporate knowledge such as the attributes of the objects and the implications of the sources .Experiments demonstrate that our al-gorithmsignificantly improves performance over existing .