PageRank-based prediction of award-winning researchers and the impact of citations

Dalibor Fiala and Gabriel Tutoky

Journal of Informetrics, 2017, vol. 11, issue 4, 1044-1068

Abstract: In this article some recent disputes about the usefulness of PageRank-based methods for the task of identifying influential researchers in citation networks are discussed. In particular, it focuses on the performance of these methods in relation to simple citation counts. With the aim of comparing these two classes of ranking methods, we analyze a large citation network of authors based on almost two million computer science papers and apply four PageRank-based and citations-based techniques to rank authors by importance throughout the period 1990–2014 on a yearly basis. We use ACM SIGMOD E. F. Codd Innovations Award and ACM A. M. Turing Award winners in our baseline lists of outstanding scientists and define four relevance weighting schemes with some predictive power for the ranking methods to increase the relevance of researchers winning in the future. We conclude that citations-based rankings perform better for Codd Award winners, but PageRank-based methods do so for Turing Award recipients when using absolute ranks and PageRank-based rankings outperform the citations-based techniques for both Codd and Turing Award laureates when relative ranks are considered. However, the two ranking groups show smaller differences if more weight is assigned to the relevance of future awardees.

Keywords: PageRank; Scholars; Citations; Rankings; Web of science; Awards (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:11:y:2017:i:4:p:1044-1068

DOI: 10.1016/j.joi.2017.09.008

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