Title: Quantifying and suppressing ranking bias in a large citation network,
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. 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.