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Learning and the structure of citation networks

  • Lafond, Francois



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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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
Date of revision:
Handle: RePEc:unm:unumer:2012071
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  1. 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.
  2. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521674096, October.
  3. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521857406, October.
  4. Pedro Albarrán & Juan A. Crespo & Ignacio Ortuño & Javier Ruiz-Castillo, 2010. "The skewness of science in 219 sub-fields and a number of aggregates," Economics Working Papers we1038, Universidad Carlos III, Departamento de Economía.
  5. 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.
  6. Benjamin F. Jones, 2005. "The Burden of Knowledge and the 'Death of the Renaissance Man': Is Innovation Getting Harder?," NBER Working Papers 11360, National Bureau of Economic Research, Inc.
  7. 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, vol. 4(2), pages 131-134, July.
  8. Pedro Albarrán & Javier Ruiz-Castillo, 2011. "References made and citations received by scientific articles," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 40-49, 01.
  9. N. Lesca, 2010. "Introduction," Post-Print halshs-00640602, HAL.
  10. Xavier Gabaix, 2008. "Power Laws in Economics and Finance," NBER Working Papers 14299, National Bureau of Economic Research, Inc.
  11. Tobias Buchman & Andreas Pyka, 2012. "Innovation Networks," Chapters, in: Handbook on the Economics and Theory of the Firm, chapter 33 Edward Elgar.
  12. Christian Ghiglino & Nicole Kuschy, 2010. "Are Patent Citations Driven by Quality?," Economics Discussion Papers 692, University of Essex, Department of Economics.
  13. Atalay, Enghin, 2013. "Sources of variation in social networks," Games and Economic Behavior, Elsevier, vol. 79(C), pages 106-131.
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