Time and Citation Networks

TL;DR

This paper introduces causally aware network analysis methods for citation networks, accounting for the temporal constraints of citations, and demonstrates their effectiveness in revealing structural differences and identifying influential citations across various domains.

Contribution

The paper presents novel causally aware network measures that incorporate the time constraint in citation networks, improving analysis of their structure and significance.

Findings

Citation networks with similar standard measures can have different causal properties.

Many citations may not be directly relevant to the citing paper.

The methods can identify influential and interdisciplinary research papers.

Abstract

Citation networks emerge from a number of different social systems, such as academia (from published papers), business (through patents) and law (through legal judgements). A citation represents a transfer of information, and so studying the structure of the citation network will help us understand how knowledge is passed on. What distinguishes citation networks from other networks is time; documents can only cite older documents. We propose that existing network measures do not take account of the strong constraint imposed by time. We will illustrate our approach with two types of causally aware analysis. We apply our methods to the citation networks formed by academic papers on the arXiv, to US patents and to US Supreme Court judgements. We show that our tools can reveal that citation networks which appear to have very similar structure by standard network measures turn out to have significantly different properties. We interpret our results as indicating that many papers in a bibliography were not directly relevant to the work and that we can provide a simple indicator of the important citations. We suggest our methods may highlight papers which are of more interest for interdisciplinary research. We also quantify differences in the diversity of research directions of different fields.