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In this paper we examine a number of methods for probing and understandingthe large-scale structure of networks that evolve over time. We focus inparticular on citation networks, networks of references between documents suchas papers, patents, or court cases. We describe three different methods ofanalysis, one based on an expectation-maximization algorithm, one based onmodularity optimization, and one based on eigenvector centrality. Using thenetwork of citations between opinions of the United States Supreme Court as anexample, we demonstrate how each of these methods can reveal significantstructural divisions in the network, and how, ultimately, the combination ofall three can help us develop a coherent overall picture of the network'sshape.

Large-scale structure of time evolving citation networks