# Bibliometric author evaluation through linear regression on the coauthor network

arXiv: https://arxiv.org/abs/1504.03115 · HTML: https://mlsanity.com/paper/1504.03115/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2015.

## Built on by (most cited first, 1 of 1)

- [Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network](https://mlsanity.com/paper/2210.06346/) (2022)

## Abstract (arXiv)

The rising trend of coauthored academic works obscures the credit assignment that is the basis for decisions of funding and career advancements. In this paper, a simple model based on the assumption of an unvarying "author ability" is introduced. With this assumption, the weight of author contributions to a body of coauthored work can be statistically estimated. The method is tested on a set of some more than five-hundred authors in a coauthor network from the CiteSeerX database. The ranking obtained agrees fairly well with that given by total fractional citation counts for an author, but noticeable differences exist.