EqRank: Theme Evolution in Citation Graphs
G. B. Pivovarov, S. E. Trunov

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.
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Text Analysis Techniques · Data Visualization and Analytics
