EqRank: Theme Evolution in Citation Graphs

TL;DR

This paper demonstrates that the EqRank algorithm produces a naturally evolving classification scheme in citation graphs, enabling detection and tracking of emerging scientific themes over time.

Contribution

It shows that EqRank's classification scheme evolves naturally and can be used to identify and monitor new scientific themes in citation networks.

Findings

EqRank yields a naturally evolving classification scheme

The scheme can detect new scientific themes

It tracks the development of themes over time

Abstract

Time evolution of the classification scheme generated by the EqRank algorithm is studied with hep-th citation graph as an example. Intuitive expectations about evolution of an adequate classification scheme for a growing set of objects are formulated. Evolution compliant with these expectations is called natural. It is demonstrated that EqRank yields a naturally evolving classification scheme. We conclude that EqRank can be used as a means to detect new scientific themes, and to track their development.