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

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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.

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Article 05 February 2021

Maurice Poirrier, Sebastián Moreno & Gonzalo Huerta-Cánepa

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Authors and Affiliations

  1. Bureau of Economic and Business Research, University of Florida, 221 Matherly Hall, Gainesville, 32611-7145, USA

    Christopher McCarty

  2. Soil and Water Science Department, University of Florida, 2169 McCarty Hall, Gainesville, FL, 32611, USA

    James W. Jawitz

  3. Department of Family and Community Medicine, University of Arizona, 1450 North Cherry Avenue, Tucson, 85724, USA

    Allison Hopkins

  4. Department of Sociology, University of Florida, 3219 Turlington Hall, Gainesville, 32611-7330, USA

    Alex Goldman

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  1. Christopher McCarty
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Correspondence to Christopher McCarty.

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McCarty, C., Jawitz, J.W., Hopkins, A. et al. Predicting author h-index using characteristics of the co-author network. Scientometrics 96, 467–483 (2013). https://doi.org/10.1007/s11192-012-0933-0

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