Distance measures for dynamic citation networks
Distance measures for dynamic citation networks
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DOI：
10.1016/j.physa.2010.06.003
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发表时间：
2010-10-01
影响因子：
3.3
通讯作者：
Fowler, James H.
中科院分区：
文献类型：
--
作者：
Bommarito, Michael J., II;Katz, Daniel Martin;Fowler, James H.
无环有向图出现在许多自然和人工过程中。在更广泛的集合中，动态引用网络代表了一种重要的无环有向图类型。例如，对此类网络的研究包括通过学术引用传播思想、通过专利引用传播创新以及普通法体系中先例的发展。产生这种非循环有向图的特定动力学不仅将它们与其他类别的图区分开来，而且还为有意义的距离度量的开发提供指导。在本文中，我们开发了汇距离测量方法，并将其与单链接层次聚类算法一起应用于二维定向优先依恋模型以及从美国最高法院前四分之一个世纪的判决中得出的经验数据。尽管应用了距离测量和聚类算法的最简单组合，但分析表明该方案产生了更准确和更可解释的聚类。 (C) 2010 Elsevier B.V. 保留所有权利。
Acyclic digraphs arise in many natural and artificial processes. Among the broader set, dynamic citation networks represent an important type of acyclic digraph. For example, the study of such networks includes the spread of ideas through academic citations, the spread of innovation through patent citations, and the development of precedent in common law systems. The specific dynamics that produce such acyclic digraphs not only differentiate them from other classes of graphs, but also provide guidance for the development of meaningful distance measures. In this article, we develop and apply our sink distance measure together with the single-linkage hierarchical clustering algorithm to both a two-dimensional directed preferential attachment model as well as empirical data drawn from the first quarter-century of decisions of the United States Supreme Court. Despite applying the simplest combination of distance measure and clustering algorithm, analysis reveals that more accurate and more interpretable clusterings are produced by this scheme. (C) 2010 Elsevier B.V. All rights reserved.