Assessing the interdependencies between scientific disciplinary profiles at the country level: A pseudo-likelihood approach

The investigation of the dynamics of national disciplinary profiles is at the forefront in quantitative investigations of science. There is an increasing number of papers that analyses the disciplinary specialization at the country level. We contribute to this literature by proposing 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 provide an illustration on data extracted from the Scopus database which relate to the national scientific production of most productive world countries for the 27 Scopus subject categories.