IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v88y2011i1d10.1007_s11192-011-0377-y.html
   My bibliography  Save this article

A study on power-law distribution of hostnames in the URL references

Author

Listed:
  • Fang Lin

    (Guangxi Normal University Library)

Abstract

The power-law distribution and the Garfield’s Law of Concentration of journal citation have long been verified by empirical data. As a relatively new type of reference, the URL references are cited more and more frequently in the scientific papers and their distribution is proved to fit for the Garfield’s Law of Concentration too. In this article, we collect three URL references datasets extracted from papers written by researchers belonging to three big research groups : Chinese Academy of Sciences, Max Planck Institute, and the whole Chinese scientific researchers. Through the curve-fitting with SPSS and contrast the results with the judgment standard of power-law distribution, we verify that there also exists power-law distribution in the citation frequency of hostnames in these three URL references datasets. And our experimental results show that the range of power exponent in the journal references and the URL references are different. Started from the concrete empirical procedures and the final experimental results, we analyze four factors that may lead to this difference between journal references and URL references: the sample size, the sampling method, the concentration of citation and the type property of citation.

Suggested Citation

  • Fang Lin, 2011. "A study on power-law distribution of hostnames in the URL references," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 191-198, July.
  • Handle: RePEc:spr:scient:v:88:y:2011:i:1:d:10.1007_s11192-011-0377-y
    DOI: 10.1007/s11192-011-0377-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-011-0377-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11192-011-0377-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ye, Fred Y. & Rousseau, Ronald, 2008. "The power law model and total career h-index sequences," Journal of Informetrics, Elsevier, vol. 2(4), pages 288-297.
    2. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    3. Siluo Yang & Junping Qiu & Zunyan Xiong, 2010. "An empirical study on the utilization of web academic resources in humanities and social sciences based on web citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(1), pages 1-19, July.
    4. Fred Y. Ye & Ronald Rousseau, 2010. "Probing the h-core: an investigation of the tail–core ratio for rank distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 431-439, August.
    5. Siluo Yang & Feng Ma & Yanhui Song & Junping Qiu, 2010. "A longitudinal analysis of citation distribution breadth for Chinese scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 755-765, December.
    6. Leo Egghe & Ronald Rousseau, 2006. "An informetric model for the Hirsch-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 121-129, October.
    7. Derek De Solla Price, 1976. "A general theory of bibliometric and other cumulative advantage processes," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(5), pages 292-306, September.
    8. Yuxian Liu & I. K. Ravichandra Rao & Ronald Rousseau, 2009. "Empirical series of journal h-indices: The JCR category Horticulture as a case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 59-74, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bertoli-Barsotti, Lucio & Lando, Tommaso, 2015. "On a formula for the h-index," Journal of Informetrics, Elsevier, vol. 9(4), pages 762-776.
    2. Perc, Matjaž, 2010. "Zipf’s law and log-normal distributions in measures of scientific output across fields and institutions: 40 years of Slovenia’s research as an example," Journal of Informetrics, Elsevier, vol. 4(3), pages 358-364.
    3. Maziar Montazerian & Edgar Dutra Zanotto & Hellmut Eckert, 2019. "A new parameter for (normalized) evaluation of H-index: countries as a case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1065-1078, March.
    4. J. Martin Zyl, 2013. "The generalized Pareto distribution fitted to research outputs of countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1099-1109, March.
    5. Tol, Richard S.J., 2013. "The Matthew effect for cohorts of economists," Journal of Informetrics, Elsevier, vol. 7(2), pages 522-527.
    6. Tokmachev, Andrey M., 2023. "Hidden scales in statistics of citation indicators," Journal of Informetrics, Elsevier, vol. 17(1).
    7. Fred Y. Ye, 2009. "An investigation on mathematical models of the h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(2), pages 493-498, November.
    8. S. R. Goldberg & H. Anthony & T. S. Evans, 2015. "Modelling citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1577-1604, December.
    9. S. Lehmann & A. D. Jackson, 2005. "Live and Dead Nodes," Computational and Mathematical Organization Theory, Springer, vol. 11(2), pages 161-170, July.
    10. Wei, Shelia X. & Tong, Tong & Rousseau, Ronald & Wang, Wanru & Ye, Fred Y., 2022. "Relations among the h-, g-, ψ-, and p-index and offset-ability," Journal of Informetrics, Elsevier, vol. 16(4).
    11. Juan E. Iglesias & Carlos Pecharromán, 2007. "Scaling the h-index for different scientific ISI fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 73(3), pages 303-320, December.
    12. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    13. Filippo Radicchi & Claudio Castellano, 2013. "Analysis of bibliometric indicators for individual scholars in a large data set," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 627-637, December.
    14. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    15. Michal Brzezinski, 2015. "Power laws in citation distributions: evidence from Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 213-228, April.
    16. Sangwal, Keshra, 2013. "Comparison of different mathematical functions for the analysis of citation distribution of papers of individual authors," Journal of Informetrics, Elsevier, vol. 7(1), pages 36-49.
    17. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2018. "The power law relationship between citation impact and multi-authorship patterns in articles in Information Science & Library Science journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 919-932, March.
    18. Fred Y. Ye & Ronald Rousseau, 2010. "Probing the h-core: an investigation of the tail–core ratio for rank distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 431-439, August.
    19. Pietro Battiston, 2014. "Citations are Forever: Modeling Constrained Network Formation," LEM Papers Series 2014/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    20. Melika Mosleh & Saeed Roshani & Mario Coccia, 2022. "Scientific laws of research funding to support citations and diffusion of knowledge in life science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1931-1951, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:88:y:2011:i:1:d:10.1007_s11192-011-0377-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.