Journal article

Quantifying and suppressing ranking bias in a large citation network

  • Vaccario, Giacomo Chair of Systems Design, ETH Zurich, Switzerland
  • Medo, Matúš Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China - Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Switzerland - Department of Physics, University of Fribourg, Switzerland
  • Wider, Nicolas Chair of Systems Design, ETH Zurich, Switzerland
  • Mariani, Manuel Sebastian Department of Physics, University of Fribourg, Switzerland - College of Computer Science and Software Engineering, Shenzhen University, Shenzhen China
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Published in:
  • Journal of Informetrics. - 2017, vol. 11, no. 3, p. 766–782
English 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.
Faculté des sciences et de médecine
Département de Physique
  • English
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