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

  • Authors:

  • Christopher Mccarty;James W. Jawitz;Allison Hopkins;Alex Goldman

  • Affiliations:

  • Bureau of Economic and Business Research, University of Florida, Gainesville, USA 32611-7145;Soil and Water Science Department, University of Florida, Gainesville, USA 32611;Department of Family and Community Medicine, University of Arizona, Tucson, USA 85724;Department of Sociology, University of Florida, Gainesville, USA 32611-7330

  • Venue:
  • Scientometrics
  • Year:
  • 2013

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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.