 Quantifying and suppressing ranking bias in a large citation networkGiacomo Vaccario, Matúš Medo, Nicolas Wider and Manuel Sebastian Mariani Journal of Informetrics, 2017, vol. 11, issue 3, 766-782 Abstract: It is widely recognized that citation counts for papers from different fields cannot be directly compared because different scientific fields adopt different citation practices. Citation counts are also strongly biased by paper age since older papers had more time to attract citations. Various procedures aim at suppressing these biases and give rise to new normalized indicators, such as the relative citation count. We use a large citation dataset from Microsoft Academic Graph and a new statistical framework based on the Mahalanobis distance to show that the rankings by well known indicators, including the relative citation count and Google's PageRank score, are significantly biased by paper field and age. Our statistical framework to assess ranking bias allows us to exactly quantify the contributions of each individual field to the overall bias of a given ranking. We propose a general normalization procedure motivated by the z-score which produces much less biased rankings when applied to citation count and PageRank score. Keywords: Impact indicators; Ranking; Network analysis; Field bias; Field normalization (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:11:y:2017:i:3:p:766-782 DOI: 10.1016/j.joi.2017.05.014 Access Statistics for this article Journal of Informetrics is currently edited by Leo Egghe More articles in Journal of Informetrics from Elsevier |
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