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Bibliometric author evaluation through linear regression on the coauthor network

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  • Persson, Rasmus A.X.

Abstract

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.

Suggested Citation

  • Persson, Rasmus A.X., 2017. "Bibliometric author evaluation through linear regression on the coauthor network," Journal of Informetrics, Elsevier, vol. 11(1), pages 299-306.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:1:p:299-306
    DOI: 10.1016/j.joi.2017.01.003
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    References listed on IDEAS

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    Cited by:

    1. Wang, Jiang-Pan & Guo, Qiang & Zhou, Lei & Liu, Jian-Guo, 2019. "Dynamic credit allocation for researchers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 208-216.
    2. Allahbakhsh, Mohammad & Amintoosi, Haleh & Behkamal, Behshid & Beheshti, Amin & Bertino, Elisa, 2021. "SCiMet: Stable, sCalable and reliable Metric-based framework for quality assessment in collaborative content generation systems," Journal of Informetrics, Elsevier, vol. 15(2).
    3. Guoliang Lyu & Ganwei Shi, 2019. "On an approach to boosting a journal’s citation potential," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1387-1409, September.

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