Abstract
The investigation of the dynamics of national disciplinary profiles is at the forefront in quantitative investigations of science. We propose a new approach to investigate the complex interactions among scientific disciplinary profiles. The approach is based on recent pseudo-likelihood techniques introduced in the framework of machine learning and complex systems. We infer, in a Bayesian framework, the network topology and the related interdependencies among national disciplinary profiles. We analyse data extracted from the Incites database which relate to the national scientific production of most productive world countries at disciplinary level over the period 1992–2016.
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