Universality of citation distributions: A new understanding

Abstract

Universality of scaled citation distributions was claimed a decade ago (F. Radicchi, S. Fortunato, and C. Castellano, PNAS, 105(45), 17268 (2008)) but its theoretical justification has been lacking so far. Here, we study citation distributions for three disciplines- Physics, Economics, and Mathematics- and assess them using our explanatory model of citation dynamics. The model posits that the citation count of a paper is determined by its fitness- the attribute, which, for most papers, is set at the moment of publication. In addition, the papers’ citation count is related to the process by which the knowledge about this paper propagates in the scientific community. Our measurements indicate that the fitness distribution for different disciplines is nearly identical and can be approximated by the log-normal distribution, while the viral propagation process is discipline-specific. The model explains which sets of citation distributions can be scaled and which can not. In particular, we show that the near-universal shape of the citation distributions for different disciplines and for different citation years traces its origin to the nearly universal fitness distribution, while deviations from this shape are associated with the discipline-specific citation dynamics of papers. Peer Review https://publons.com/publon/10.1162/qss_a_00127

A network-based normalized impact measure reveals successful periods of scientific discovery across disciplines

“…S4 ). This result is in line with numerous prior studies that identified a similar range of for papers in journals ( 18 – 20 ), institutions ( 20 ), fields ( 21 – 23 ), as well as Mendeley readerships ( 24 ) and patent citations ( 25 ).…”

Overton -- A bibliometric database of policy document citations

“…For example, for DOIs published between 2010-14 (n = 731, 696), 425, 268 are cited only once (58%), and only 25, 190 are cited 10 or more times (3.4%). Prior research using conventional bibliographic databases have investigated possible statistical distributions that model citation data (Brzezinski, 2015;Eom & Fortunato, 2011;Golosovsky, 2021;Thelwall, 2016), although there is some disagreement on whether powerlaw, log-normal or negative binomial distributions are best. Results vary depending on time period and discipline analysed, database used, and if documents with zero citations are included.…”

Uncited papers are not useless

“…How can one measure these functions? The fitness distribution can be determined from the analysis of citation distributions in different citation windows (Golosovsky, 2021) while M dir (t) can be found from the analysis of the direct and indirect citations garnered by the papers in the collection. On another hand, M dir (t) can be found from the analysis of the reference lists of papers.…”

“…the remaining parameters of citation dynamics, we used our full stochastic model and the previously found aging function A(t) (Golosovsky, 2021) and fitted the citation distributions for each category in the year 2017. We assumed the log-normal reduced fitness distribution and used its width σ as a fitting parameter.…”

“…Our objective is to validate this statement. Indeed, our recently developed model (Golosovsky, 2019(Golosovsky, , 2021Golosovsky & Solomon, 2017) captures many attributes of the citation dynamics of research papers, including citation trajectories and citation distributions. In this study, we demonstrate that this model quantitatively captures the uncitedness ratio for three single disciplines and four broad scientific categories.…”