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

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

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 PageRankbased and citations-based techniques to rank authors by importance throughout the period 1990ÔÇô2014 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.

This is a preprint version of the article.

Keywords: PageRank, Scholars, Citations, Rankings, Web of Science, Awards

Year: 2017

Journal ISSN: 1751-1577
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Authors of this publication:

Dalibor Fiala

Phone: +420 377 63 2429

Dalibor is the research group coordinator and an associate professor at the Department of Computer Science and Engineering at the University of West Bohemia in Pilsen, Czech Republic. He is interested in data mining, web mining, information retrieval, informetrics, and information science.

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Authors:  Karel Je┼żek, Dalibor Fiala, Michal Nykl
Desc.:Application of the PageRank algorithm and its modifications to the exploration of network structures, particularly citation and co-autorship networks.