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Learning and the structure of citation networks

  • Lafond, Francois

    ()

    (UNU-MERIT/MGSoG)

The distribution of citations received by scientific publications can be approximated by a power law, a finding that has been explained by “cumulative advantage”. This paper argues that socially embedded learning is a plausible mechanism behind this cumulative advantage. A model assuming that scientists face a time trade-off between learning and writing papers, that they learn the papers known by their peers, and that they cite papers they know, generates a power law distribution of popularity, and a shifted power law for the distribution of citations received. The two distributions flatten if there is relatively more learning. The predicted exponent for the distribution of citations is independent of the average in-(or out-) degree, contrary to an untested prediction of the reference model (Price, 1976). Using publicly available citation networks, an estimate of the share of time devoted to learning (against producing) is given around two thirds.

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File URL: http://www.merit.unu.edu/publications/wppdf/2012/wp2012-071.pdf
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Paper provided by United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT) in its series MERIT Working Papers with number 071.

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Date of creation: 2012
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Handle: RePEc:unm:unumer:2012071
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  1. Albarrán, Pedro & Ruiz-Castillo, Javier, 2009. "References made and citations received by scientific articles," UC3M Working papers. Economics we094581, Universidad Carlos III de Madrid. Departamento de Economía.
  2. Albarrán, Pedro & Ruiz-Castillo, Javier & Ortuño, Ignacio & Crespo, Juan A., 2010. "The skewness of science in 219 sub-fields and a number of aggregates," UC3M Working papers. Economics we1038, Universidad Carlos III de Madrid. Departamento de Economía.
  3. Atalay, Enghin, 2013. "Sources of variation in social networks," Games and Economic Behavior, Elsevier, vol. 79(C), pages 106-131.
  4. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, 05.
  5. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521674096, june. pag.
  6. Benjamin F. Jones, 2009. "The Burden of Knowledge and the "Death of the Renaissance Man": Is Innovation Getting Harder?," Review of Economic Studies, Oxford University Press, vol. 76(1), pages 283-317.
  7. A. Pyka, 2007. "Innovation Networks," Chapters, in: Elgar Companion to Neo-Schumpeterian Economics, chapter 23 Edward Elgar Publishing.
    • Tobias Buchman & Andreas Pyka, 2012. "Innovation Networks," Chapters, in: Handbook on the Economics and Theory of the Firm, chapter 33 Edward Elgar Publishing.
  8. N. Lesca, 2010. "Introduction," Post-Print halshs-00640602, HAL.
  9. 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.
  10. Matthew O. Jackson & Brian W. Rogers, 2007. "Meeting Strangers and Friends of Friends: How Random Are Social Networks?," American Economic Review, American Economic Association, vol. 97(3), pages 890-915, June.
  11. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521857406, june. pag.
  12. Bramoullé, Yann & Currarini, Sergio & Jackson, Matthew O. & Pin, Paolo & Rogers, Brian W., 2012. "Homophily and long-run integration in social networks," Journal of Economic Theory, Elsevier, vol. 147(5), pages 1754-1786.
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