On the Stability of Community Detection Algorithms on Longitudinal Citation Data
Michael James Bommarito II
Bommarito Consulting, LLC
Daniel Martin Katz
Michigan State University - College of Law
University of Michigan at Ann Arbor - Center for Study of Complex Systems
August 2, 2009
Procedia Social and Behavioral Sciences, 2010
Proceedings of the 6th Conference on Applications of Social Network Analysis (ASNA 2009)
There are fundamental differences between citation networks and other classes of graphs. In particular, given that citation networks are directed and acyclic, methods developed primarily for use with undirected social network data may face obstacles. This is particularly true for the dynamic development of community structure in citation networks. Namely, it is neither clear when it is appropriate to employ existing community detection approaches nor is it clear how to choose among existing approaches. Using simulated data, we attempt to clarify the conditions under which one should use existing methods and which of these algorithms is appropriate in a given context. We hope this paper will serve as both a useful guidepost and an encouragement to those interested in the development of more targeted approaches for use with longitudinal citation data.
Number of Pages in PDF File: 12
Keywords: community detection, evolutionary graph theory, computational legal studies, citation network, direct acyclic graphs, graph theory, network analysis and law, judicial citation network, network dynamics
JEL Classification: C60, C61, C63, C88, C8Accepted Paper Series
Date posted: August 4, 2009 ; Last revised: May 26, 2010
© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo7 in 0.282 seconds