ArticleScientometrics
Power laws in citation distributions: evidence from Scopus.
Article in Scientometrics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Global trends in tumor-associated neutrophil research: a bibliometric and visual analysis.Frontiers in immunology · 2025Pooled it
- An Extensive Knowledge Mapping Review of Measurement and Validity in Language Assessment and SLA Research.Frontiers in psychology · 2020Pooled it
- Political expression of academics on Twitter.Nature human behaviour · 2025Article
- Clinical Impact of "Real World Data" and Blockchain on Public Health: A Scoping Review.International journal of environmental research and public health · 2024Article
- A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.Nature communications · 2020Article
- Neutral syndrome.Nature human behaviour · 2020Review
- Three dimensions of scientific impact.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Science through Wikipedia: A novel representation of open knowledge through co-citation networks.PloS one · 2020Article
- Re-evaluating Phoneme Frequencies.Frontiers in psychology · 2020Article
- On entropy research analysis: cross-disciplinary knowledge transfer.Scientometrics · 2018Article
- A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.Scientific reports · 2017Article
- A bibliometric analysis of two decades of aromatherapy research.BMC research notes · 2017Article
- Article
- Science and Facebook: The same popularity law!PloS one · 2017Article
- The impact of structural genomics: the first quindecennial.Journal of structural and functional genomics · 2016Review
- What Is a Complex Innovation System?PloS one · 2016Article
- Towards a simple mathematical theory of citation distributions.SpringerPlus · 2015Article
- On a heuristic point of view concerning the citation distribution: introducing the Wakeby distribution.SpringerPlus · 2015Article
Corrections and comments
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Authors and funding
1 author.
Funding
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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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.