EXPLORING AND UNDERSTANDING CITATION-BASED SCIENTIFIC METRICS

Mikalai Krapivin (), Maurizio Marchese and Fabio Casati
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Mikalai Krapivin: Department of Information Engineering and Computer Science, University of Trento, Trento, 38100, Italy
Maurizio Marchese: Department of Information Engineering and Computer Science, University of Trento, Trento, 38100, Italy
Fabio Casati: Department of Information Engineering and Computer Science, University of Trento, Trento, 38100, Italy

Advances in Complex Systems (ACS), 2010, vol. 13, issue 01, 59-81

Abstract: This paper explores citation-based metrics, how they differ in ranking papers and authors, and why. We initially take as example three main metrics that we believe significant; the standard citation count, the more and more popularh-index, and a variation we propose of PageRank applied to papers (called PaperRank), that is appealing as it mirrors proven and successful algorithms for ranking web pages. As part of analyzing them, we develop generally applicable techniques and metrics for qualitatively and quantitatively analyzing indexes that evaluate content and people, as well as for understanding the causes of their different behaviors. Finally, we extend the analysis to other popular indexes, to show whether the choice of the index has a significant effect in how papers and authors are ranked. We put the techniques at work on a dataset of over 260 K ACM papers, and discovered that the difference in ranking results is indeed very significant (even when restricting to citation-based indexes), with half of the top-ranked papers differing in a typical 20-element long search result page for papers on a given topic, and with the top researcher being ranked differently over half of the times in an average job posting with 100 applicants.

Keywords: PageRank; scientometrics; citation analyses (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219525910002487

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