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Power laws in citation distributions: Evidence from Scopus

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  • Michał Brzeziński

    (Faculty of Economic Sciences, University of Warsaw)

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.

Suggested Citation

  • Michał Brzeziński, 2014. "Power laws in citation distributions: Evidence from Scopus," Working Papers 2014-05, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2014-05
    as

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    File URL: http://www.wne.uw.edu.pl/inf/wyd/WP/WNE_WP122.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    power law; Pareto model; citation distribution; bibliometrics; scientometrics; Scopus; model selection;
    All these keywords.

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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