Template-type: ReDIF-Paper 1.0
Author-Name: Lafond, Francois
Author-Email: lafond@merit.unu.edu
Author-Workplace-Name: UNU-MERIT/MGSoG
Title: Learning and the structure of citation networks  
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. 
Classification-JEL: D83, D85, O31, O33
Keywords: shifted power law, scale free networks, two-mode networks, 
	 cumulative advantage, polynomial attachment kernel, innovation, 
	 diffusion.
Series: UNU-MERIT Working Papers
Creation-Date: 2012
Number: 071
File-URL: http://www.merit.unu.edu/publications/wppdf/2012/wp2012-071.pdf
File-Format: application/pdf
File-Size: 275 Kb
Handle: RePEc:unm:unumer:2012071