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Cited by 196scientometrics and bibliometrics researchWeb visibility and informetricsPsychology Research and Bibliometrics

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Power laws in citation distributions: evidence from Scopus

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Brzezinski, Michal·2015·Scientometrics
DOI 10.1007/s11192-014-1524-zPMC PMC4365275
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Abstract

Modeling distributions of citations to scientific papers is crucial for understanding how science develops. However, there is a considerable empirical controversy on which statistical model fits the citation distributions best. This paper is concerned with rigorous empirical detection of power-law behaviour in the distribution of citations received by the most highly cited scientific papers. We have used a large, novel data set on citations to scientific papers published between 1998 and 2002 drawn from Scopus. The power-law model is compared with a number of alternative models using a likelihood ratio test. We have found that the power-law hypothesis is rejected for around half of the Scopus fields of science. For these fields of science, the Yule, power-law with exponential cut-off and log-normal distributions seem to fit the data better than the pure power-law model. On the other hand, when the power-law hypothesis is not rejected, it is usually empirically indistinguishable from most of the alternative models. The pure power-law model seems to be the best model only for the most highly cited papers in "Physics and Astronomy". Overall, our results seem to support theories implying that the most highly cited scientific papers follow the Yule, power-law with exponential cut-off or log-normal distribution. Our findings suggest also that power laws in citation distributions, when present, account only for a very small fraction of the published papers (less than 1 % for most of science fields) and that the power-law scaling parameter (exponent) is substantially higher (from around 3.2 to around 4.7) than found in the older literature.

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Fig. 1 The complementary cumulative distribution functions (blue circles) and best power-law fits (dashed black line) for citation distributions that did not pass the goodness-of-fit test, Scopus, 1998–2002, 5-year citation window
Fig. 1 The complementary cumulative distribution functions (blue circles) and best power-law fits (dashed black line) for citation distributions that did not pass the goodness-of-fit test, Scopus, 1998–2002, 5-year citation windowCC BY · PubMed Central

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Research works printed but not linked: 1

  1. Albarrán, P., Crespo, J.A., Ortuño, I., & Ruiz-Castillo, J. (2011). The skewness of science in 219 sub-fields and a number of aggregates. Working paper 11–09, Universidad Carlos III. · DOI

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Metadata

Authors
Brzezinski, M.
Venue
Scientometrics
Publisher
Springer Science and Business Media LLC
Published
2015-01-22
Volume
103
Issue
1
Pages
213-228
ISSN
  • 0138-9130 (ISSN-L)
  • 1588-2861
DOI
10.1007/s11192-014-1524-z
PMC
PMC4365275
OpenAlex
W2170265466
Cited by
196
References
36
Citations by year
2026: 8 · 2025: 24 · 2024: 21 · 2023: 15 · 2022: 14 · 2021: 17 · 2020: 40 · 2019: 10 · 2018: 13 · 2017: 14 · 2016: 14 · 2015: 6
FWCI
14.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientometrics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion, cc-by)
Topics
  • Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
  • Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
Keywords
citation distribution, power law, highly cited papers, lognormal distribution, likelihood ratio test, model comparison, Scopus, scientific impact

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