Large-scale structure of time evolving citation networks

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dc.creatorLeicht, E. A.
dc.creatorClarkson, Gavin
dc.creatorShedden, Kerby
dc.creatorNewman, M. E. J.
dc.date2007-05-31
dc.date2007-06-06
dc.date.accessioned2026-07-07T08:36:44Z
dc.date.available2026-07-07T08:36:44Z
dc.descriptionIn this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network, and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape.
dc.description10 pages, 6 figures; journal names for 4 references fixed
dc.identifierhttps://arxiv.org/abs/0706.0015
dc.identifierhttp://arxiv.org/abs/0706.0015
dc.identifierEur. Phys. J. B 59, 75-83 (2007)
dc.identifierdoi:10.1140/epjb/e2007-00271-7
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/140166
dc.subjectPhysics and Society
dc.subjectStatistical Mechanics
dc.titleLarge-scale structure of time evolving citation networks
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