Michael James Bommarito II, Daniel Martin Katz, & Jon Zelner, all of the University of Michigan at Ann Arbor, Center for Study of Complex Systems, & Professor James H. Fowler of the University of California, San Diego, Department of Political Science, have published Distance Measures for Dynamic Citation Networks on SSRN. Here is the abstract:
“Acyclic digraphs arise in many natural and artificial processes. Among
the broader set, dynamic citation networks represent a substantively
important form of acyclic digraphs. 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 meaningful distance measures for these networks. We apply our sink based distance measure and the single-linkage hierarchical clustering algorithm to the first quarter century of decisions of the United States Supreme Court. Despite applying the simplest distance measure and a straight forward clustering algorithm, qualitative analysis reveals that accurate clusterings are produced by this scheme.”
Tags: Acyclic Digraphs, Citation Networks, Citations to United States Supreme Court decisions, Clustering, Community Detection, Distance Measures, Judicial Citations, Legal citation studies, Legal citations, Patent Citations, Statistical analysis of citations to court decisions, Statistical analysis of citations to United States Supreme Court decisions, Statistical analysis of legal citations, Statistical analysis of legal information, United States Supreme Court citations
