Co-authorship networks and scientific performance: an empirical analysis using the generalized extreme value distribution

Domenico De Stefano and Susanna Zaccarin

Journal of Applied Statistics, 2016, vol. 43, issue 1, 262-279

Abstract: This paper aims to explore the effects of collaborative behaviour on scholar scientific performance. Individual network measures related to scholar centrality as well as attitude to collaborate with others are derived from co-authorship networks in a given scientific community (i.e. Italian academic statisticians). Co-authorship information have been collected from three data sources of national-based, discipline-based, and international-based high-impact publications. Both network and individual covariates are used to model individual h -index by generalized extreme value distribution. Results show a positive association between performance and actors' central position in the network. Having a large number of co-authors and occupying central positions are likely to positively affect scientific performance.

Date: 2016
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DOI: 10.1080/02664763.2015.1017719

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