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DepRank: A Probabilistic Measure of Dependence via Heterogeneous Links

机译：DepRank：通过异构链接的概率依赖性度量

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摘要

Dependence is a common relationship between objects. Many works have paid their attentions on dependence, but many of them mainly focus on constructing or exploiting dependence graphs on some specific domain. In this paper, we give a generic definition of dependence and make equivalence to the situation of graphs, and propose an algorithm called DepRank, which quantifies the dependence degree of a node pair based on probabilities of all dependence paths. Empirical study shows that DepRank method has reasonable results and maintains a well balance between accuracy and efficiency.
机译：依赖关系是对象之间的常见关系。许多作品都将注意力放在依赖上，但是许多作品主要集中于在某些特定领域上构造或利用依赖图。在本文中，我们给出了相关性的一般定义，并等效于图的情况，并提出了一种称为DepRank的算法，该算法根据所有相关性路径的概率来量化节点对的相关性程度。实证研究表明，DepRank方法具有合理的结果，并且在准确性和效率之间保持了很好的平衡。

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  • 来源
  • 会议地点 Suzhou(CN)
  • 作者

    Pei Li; Bo Hu; Hongyan Liu; Jun He; Xiaoyong Du;

  • 作者单位

    Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China School of Information, Renmin University of China, Beijing, China;

    Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China School of Information, Renmin University of China, Beijing, China;

    Department of Management Science and Engineering, Tsinghua University, Beijing, China;

    Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China School of Information, Renmin University of China, Beijing, China;

    Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China School of Information, Renmin University of China, Beijing, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机网络;
  • 关键词

    link graph; probabilistic measure; object dependence;

    机译：链接图;概率测度对象依赖;

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