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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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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 & Crespo, Juan A. & Ortuño-Ortín, Ignacio & Ruiz-Castillo, Javier, 2010. "The Skewness of Science in 219 Sub-Fields and a Number of Aggregates," CEPR Discussion Papers 8126, C.E.P.R. Discussion Papers.
  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, vol. 4(2), pages 131-134, July.
  3. Benjamin F. Jones, 2005. "The burden of knowledge and the ‘death of the Renaissance man’: Is innovation getting harder?," Proceedings, Federal Reserve Bank of San Francisco.
  4. Pedro Albarrán & Javier Ruiz-Castillo, 2009. "References made and citations received by scientific articles," Economics Working Papers we094581, Universidad Carlos III, Departamento de Economía.
  5. Xavier Gabaix, 2008. "Power Laws in Economics and Finance," NBER Working Papers 14299, National Bureau of Economic Research, Inc.
  6. A. Pyka, 2007. "Innovation Networks," Chapters, in: Elgar Companion to Neo-Schumpeterian Economics, chapter 23 Edward Elgar.
    • Tobias Buchman & Andreas Pyka, 2012. "Innovation Networks," Chapters, in: Handbook on the Economics and Theory of the Firm, chapter 33 Edward Elgar.
  7. Christian Ghiglino & Nicole Kuschy, 2010. "Are Patent Citations Driven by Quality?," Economics Discussion Papers 692, University of Essex, Department of Economics.
  8. Atalay, Enghin, 2013. "Sources of variation in social networks," Games and Economic Behavior, Elsevier, vol. 79(C), pages 106-131.
  9. 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.
  10. 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.
  11. repec:cup:cbooks:9780521857406 is not listed on IDEAS
  12. repec:cup:cbooks:9780521674096 is not listed on IDEAS
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