Large-scale structure of time evolving citation networks

TL;DR

This paper explores three analytical methods—expectation-maximization, modularity optimization, and eigenvector centrality—to understand the large-scale, evolving structure of citation networks, exemplified by US Supreme Court opinions.

Contribution

It introduces and compares three different analytical approaches for revealing structural divisions in dynamic citation networks, enhancing understanding of their overall shape.

Findings

All three methods reveal significant structural divisions.

Combining methods provides a coherent picture of network structure.

Application to Supreme Court citations demonstrates practical utility.

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.