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

Author

Listed:
  • Christopher McCarty

    () (University of Florida)

  • James W. Jawitz

    () (University of Florida)

  • Allison Hopkins

    () (University of Arizona)

  • Alex Goldman

    () (University of Florida)

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.

Suggested Citation

  • Christopher McCarty & James W. Jawitz & Allison Hopkins & Alex Goldman, 2013. "Predicting author h-index using characteristics of the co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 467-483, August.
  • Handle: RePEc:spr:scient:v:96:y:2013:i:2:d:10.1007_s11192-012-0933-0
    DOI: 10.1007/s11192-012-0933-0
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    References listed on IDEAS

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    4. Xiaoling Sun & Hongfei Lin & Kan Xu & Kun Ding, 2015. "How we collaborate: characterizing, modeling and predicting scientific collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 43-60, July.

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