Predicting author h-index using characteristics of the co-author network Academic Article uri icon

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. 2012 Akadmiai Kiad, Budapest, Hungary.

published proceedings

  • Scientometrics

altmetric score

  • 0.5

author list (cited authors)

  • McCarty, C., Jawitz, J. W., Hopkins, A., & Goldman, A.

citation count

  • 49

complete list of authors

  • McCarty, Christopher||Jawitz, James W||Hopkins, Allison||Goldman, Alex

publication date

  • August 2013