Promise and Pitfalls of Extending Google's PageRank Algorithm to Citation Networks
Sergei Maslov, S. Redner
TL;DR
This paper explores extending Google's PageRank algorithm to citation networks to identify influential scientific publications and trending research areas, highlighting benefits and potential pitfalls of relying on quantitative metrics.
Contribution
The paper demonstrates the application of PageRank and CiteRank algorithms to scientific citation networks, offering a novel approach to assess scientific impact and popularity.
Findings
PageRank-based metrics identify influential papers more effectively than citation counts.
CiteRank helps discover currently popular research directions.
Potential pitfalls include over-reliance on quantitative measures for scientific quality.
Abstract
We review our recent work on applying the Google PageRank algorithm to find scientific "gems" among all Physical Review publications, and its extension to CiteRank, to find currently popular research directions. These metrics provide a meaningful extension to traditionally-used importance measures, such as the number of citations and journal impact factor. We also point out some pitfalls of over-relying on quantitative metrics to evaluate scientific quality.
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