Centrality metrics and graduate program evolution: The case of production engineering graduate programs in Brazil Ricardo Lopes de Andrade, Leandro Chaves Rêgo Pesquisa Operacional, 2019 In Brazil, graduate courses are evaluated by the National Graduate Program System and regulated by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), an agency linked to the Ministry of Education (MEC). The intellectual production of the faculty of the programs is the main criteria in determining a program’s grade. In this study, we verified whether the grade attributed to the programs is dependent on the co-authorship network of the faculty of the program. Particularly, we analyze whether programs composed mostly by faculty members who cooperate in academic productions and have a more central position in the co-authorship network perform better than those with faculty with fewer collaboration. The paper concludes that there is a relation between the programs’ grade and the number of faculty members that collaborates in their intellectual productions. It is also concluded that programs that improved the grade are composed mainly of faculty members with high centrality or have few faculty members with low centrality measures. Moreover, programs that have decreased their grade are formed mainly by faculty members with low centrality measures or with few faculty members with high centrality measures.
p-means centrality Ricardo Lopes de Andrade, Leandro Chaves Rêgo Communications in Nonlinear Science and Numerical Simulation, 2019
Exploring the co-authorship network among CNPq’s productivity fellows in the area of industrial engineering Ricardo Lopes de Andrade, Leandro Chaves Rêgo Pesquisa Operacional, 2017 In this article, we have built a co-authorship network among researchers with CNPQ grant in research productivity (PQ) in the area of Industrial Engineering and analyze which Social Network Analysis metrics impact their productivity level. Unlike other studies that mostly analyze unweighted networks, ours explored more broadly the network since the metrics were calculated in three ways: unweighted, including the edges weights and including the edges and nodes' attributes. Thus, the generated results are more precise and detailed since more information is obtained. We consider the h-index of the researchers as the nodes' attributes and measured the impact using Kendall correlation. We show that geographical distance is still a barrier to collaboration among PQs in this area and that collaboration with researchers with different levels of grant has the greatest impact in the level of the grant a researcher has.
