Learning and the structure of citation networks
Lafond, François (2012). Learning and the structure of citation networks. UNU-MERIT.
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Author Lafond, François Title Learning and the structure of citation networks Publication Date 2012 Publisher UNU-MERIT Abstract 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. Keyword Shifted power law
Scale free networks
Two-mode networks
Cumulative advantage
Polynomial attachment kernel
Innovation
DiffusionJEL D83
D85
O31
O33Copyright Holder UNU-MERIT Copyright Year 2012 -
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