Large-scale structure of time evolving citation networks |
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| Authors: | E. A. Leicht G. Clarkson K. Shedden M. E.J. Newman |
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| Affiliation: | (1) Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA;(2) School of Information, University of Michigan, Ann Arbor, MI 48109, USA;(3) Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA;(4) Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA |
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| Abstract: | ![]()
In 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. |
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| Keywords: | 89.75.Hc Networks and genealogical trees 89.75.Fb Structures and organization in complex systems 89.70.+c Information theory and communication theory |
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