# Quantifying and suppressing ranking bias in a large citation network

## TL;DR

This paper analyzes biases in citation-based rankings caused by field and age differences, and proposes a normalization method that reduces these biases for more equitable evaluation.

## Contribution

It introduces a new statistical framework using Mahalanobis distance to quantify biases and a normalization procedure inspired by z-scores to mitigate them.

## Key findings

- Citation rankings are significantly biased by field and age.
- The proposed normalization reduces bias in citation and PageRank scores.
- Normalized indicators provide more equitable comparisons across papers.

## 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.