Mitigating ageing bias in article level metrics using citation network analysis

István Tóth, Zsolt I. Lázár, Levente Varga, Ferenc Járai-Szabó, István Papp, Răzvan V. Florian and Mária Ercsey-Ravasz

Journal of Informetrics, 2021, vol. 15, issue 1

Abstract: Article level scientometric indicators (ALMs) are usually of cumulative nature making articles of different age hard to compare. Here, we introduce a new ALM, the Time Debiased Significance Score (TDSS), which measures the significance of a publication based on the structure of the whole citation network and eliminates the global ageing bias in the network: older publications should not be a priori privileged or disadvantaged compared to newer ones. The TDSS is based on a modified variant of the PageRank measure, incorporating a mathematically consistent temporal detrending and ensuring a few key features: (i) the TDSS should not show any global trend as a function of the topological index (causal order in the citation network); (ii) the TDSS value of a publication should decrease as time passes (and the citation network grows) if no more citations are associated with it. The above definition is beneficial in multiple ways, including e.g. low computational complexity and weak domain dependence. Further, estimation of reliability of the TDSS and its extension to groups of items like overall score of a research group are also possible.

Keywords: Scientometric indicator; Article level metric; Citation network; Ageing bias (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157720306222
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:15:y:2021:i:1:s1751157720306222

DOI: 10.1016/j.joi.2020.101105

Access Statistics for this article

Journal of Informetrics is currently edited by Leo Egghe

More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().