Predicting author h-index using characteristics of the co-author network

Christopher McCarty (), James W. Jawitz (), Allison Hopkins () and Alex Goldman ()
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Christopher McCarty: University of Florida
James W. Jawitz: University of Florida
Allison Hopkins: University of Arizona
Alex Goldman: University of Florida

Scientometrics, 2013, vol. 96, issue 2, No 5, 467-483

Abstract: Abstract The objective of this work was to test the relationship between characteristics of an author’s network of coauthors to identify which enhance the h-index. We randomly selected a sample of 238 authors from the Web of Science, calculated their h-index as well as the h-index of all co-authors from their h-index articles, and calculated an adjacency matrix where the relation between co-authors is the number of articles they published together. Our model was highly predictive of the variability in the h-index (R 2 = 0.69). Most of the variance was explained by number of co-authors. Other significant variables were those associated with highly productive co-authors. Contrary to our hypothesis, network structure as measured by components was not predictive. This analysis suggests that the highest h-index will be achieved by working with many co-authors, at least some with high h-indexes themselves. Little improvement in h-index is to be gained by structuring a co-author network to maintain separate research communities.

Keywords: Egocentric network; H-index; Co-author network (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (17)

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DOI: 10.1007/s11192-012-0933-0

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