摘要翻译：
我们介绍了一系列新的排序算法，称为ERank，它运行在线性/近似线性时间，并建立在显式建模网络作为不确定证据的基础上。该模型采用概率论证系统(PAS)，它是概率论和命题逻辑的结合，也是Dempster-Shafer证据理论的特例。ERank快速生成NP完全问题的近似结果，使得该技术能够在大型网络中使用。我们对引文网络使用前面介绍的PAS模型，将其推广到所有网络。在聚类有效性检验的基础上，提出了一种统计检验方法，用于比较不同排序算法的性能。我们在现实网络中使用该测试进行的实验表明，与PageRank、closeness和Betweenness等知名算法相比，ERank具有最好的性能。
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英文标题：
《Use of Rapid Probabilistic Argumentation for Ranking on Large Complex
  Networks》
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作者：
Burak Cetin, Haluk Bingol
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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英文摘要：
  We introduce a family of novel ranking algorithms called ERank which run in linear/near linear time and build on explicitly modeling a network as uncertain evidence. The model uses Probabilistic Argumentation Systems (PAS) which are a combination of probability theory and propositional logic, and also a special case of Dempster-Shafer Theory of Evidence. ERank rapidly generates approximate results for the NP-complete problem involved enabling the use of the technique in large networks. We use a previously introduced PAS model for citation networks generalizing it for all networks. We propose a statistical test to be used for comparing the performances of different ranking algorithms based on a clustering validity test. Our experimentation using this test on a real-world network shows ERank to have the best performance in comparison to well-known algorithms including PageRank, closeness, and betweenness.
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PDF链接：
https://arxiv.org/pdf/0802.3293