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, presented their paper entitled Distance Measures for Dynamic Citation Networks at the Political Networks Conference 2010, held 19-21 May 2010 at the Duke University Department of Political Science, in Durham, North Carolina, USA.
Here is the abstract of the paper:
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 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 measures and clustering algorithms, analysis reveals that more accurate and more interpretable clusterings are produced by this scheme.