Brazilian Journal of Probability and Statistics

Brazilian network of PhDs working with probability and statistics

Luciano Digiampietri, Leandro Rêgo, Filipe Costa de Souza, Raydonal Ospina, and Jesús Mena-Chalco

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text


Statistical and probabilistic reasoning enlightens our judgments about uncertainty and the chance or beliefs on the occurrence of random events in everyday life. Therefore, there are scientists working with Probability and Statistics in various fields of knowledge, what favors the formation of scientific network collaborations of researchers with different backgrounds. Here, we propose to describe the Brazilian PhDs who work with probability and statistics. In particular, we analyze national and states collaboration networks of such researchers by calculating different metrics. We show that there is a greater concentration of nodes in and around the cites which host Probability and Statistics graduate programs. Moreover, the states that host P&S Doctoral programs are the most central. We also observe a disparity in the size of the states networks. The clustering coefficient of the national network suggests that this network and regional differences especially with respect to states from South-east and North is not cohesive and, probably, it is in a maturing stage.

Article information

Braz. J. Probab. Stat., Volume 32, Number 4 (2018), 755-782.

Received: April 2016
Accepted: April 2017
First available in Project Euclid: 17 August 2018

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Academic collaboration CNPq’s productivity research fellows probability and statistics social network analysis


Digiampietri, Luciano; Rêgo, Leandro; Costa de Souza, Filipe; Ospina, Raydonal; Mena-Chalco, Jesús. Brazilian network of PhDs working with probability and statistics. Braz. J. Probab. Stat. 32 (2018), no. 4, 755--782. doi:10.1214/17-BJPS362.

Export citation


  • Abbasi, A., Altmann, J. and Hossain, L. (2011). Identifying the effecs of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance mesuares and social network analysis mesuares. Journal of Informetrics 5, 594–607.
  • Alves, A. D., Yanasse, H. H. and Soma, N. Y. (2014). Perfil dos bolsistas PQ da Área de Química baseado na Plataforma lattes. Química Nova 37, 377–383.
  • Andretta, P. I. (2012). Uma análise sobre a produção, produtividade e colaboração na ciência da informação no Brasil entre os anos 2007 a 2009. Palabra Clave 1, 48–52.
  • Andretta, P. I., Silva, E. and Ramos, R. (2012). Aproximações sobre produção, produtividade e colaboração científica entre os departamentos de ciência da informação do estado de São Paulo. RDBCI 9, 46–63.
  • Ara, A. and Louzada, F. (2012). Descrição de algumas das dimensões que compõem o perfil do corpo docente dos departamentos de estatística do Brasil. Boletim de Educação Matemática 26(42A), 23–38.
  • Arruda, D., Bezerra, F., Neris, V., Rocha De Toro, P. and Wainera, J. (2009). Brazilian computer science research: Gender and regional distributions. Scientometrics 79, 651–665.
  • Baccini, A., Barabesi, L. and Marcheselli, M. (2009). How are statistical journals linked? A network analysis. Chance 22, 35–45.
  • Bellotti, E. (2012). Getting funded. Multi-level network of physicists in Italy. Social Networks 34, 215–229.
  • Bojanowski, M. and Corten, R. (2014). Measuring segregation in social networks. Social Networks 39, 14–32.
  • Bonacich, P. and Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks 23, 191–201.
  • Bordons, M., Aparicio, J., González-Albo, B. and Díaz-Faes, A. A. (2015). The relationship between the research performance of scientists and their position in co-authorship networks in three fields. Journal of Informetrics 9, 135–144.
  • Cimenler, O., Reeves, K. A. and Skvoretz, J. (2014). A regression analysis of researchers’ social network metrics on their citation performance in college of engineering. Journal of Informetrics 8, 667–682.
  • Costa, B. M. G., da Silva Pedro, E. and de Macedo, G. R. (2013). Scientific collaboration in biotechnology: The case of the northeast region in Brazil. Scientometrics 95, 571–592.
  • de Arruda, G. F., Peron, T. K. D., de Andrade, M. G., Achcar, J. A. and Rodrigues, F. A. (2013). The influence of network properties on the synchronization of Kuramoto oscillators quantified by a Bayesian regression analysis. Journal of Statistical Physics 152, 519–533.
  • De Stefano, D., Giordano, G. and Vitale, M. P. (2011). Issues in the analysis of co-authorship networks. Quality and Quantity 45, 1091–1107.
  • Digiampietri, L., Mena-Chalco, J., Silva, G. S., Oliveira, L., Malheiro, A. and Meira, D. (2012). Dinâmica das relações de coautoria nos programas de pós-graduação em computação no Brasil. In I Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2012).
  • Digiampietri, L. A. and da Silva, E. E. (2011). A framework for social network of researchers analysis. Iberoamerican Journal of Applied Computing 1, 1–24.
  • Digiampietri, L. A., Mena-Chalco, J. P., Melo, P. O. V., Malheiros, A. P., Meira, D. N. O., Franco, L. F. and Oliveira, L. B. (2014). BraX-ray: An X-ray of the Brazilian computer science graduate programs. PLoS ONE 9, 20. DOI:10.1371/journal.pone.0094541.
  • Easley, D. and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge, MA: Cambridge University Press.
  • Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks 1, 215–239.
  • Glänzel, W. and Schubert, A. (2005) Handbook of Quantitative Science and Technology Research: The Use of Publication and Patent Statistics in Studies of S&T Systems, Chapter Analysing Scientific Networks Through Co-Authorship, 257–276. Dordrecht: Springer.
  • Jackson, M. O. (2008). Social and Economic Networks. Princeton, NJ: Princeton University Press.
  • Katz, J. S. and Martin, B. R. (1997). What is research collaboration? Research Policy 26, 1–18.
  • Krackhardt, D. and Stern, R. (1988). Informal networks and organizational crises: An experimental simulation. Social Psychology Quarterly 51, 123–140.
  • Latapy, M., Magnien, C. and Vecchio, N. D. (2008). Basic notions for the analysis of large two-mode networks. Social Networks 30, 31–48.
  • Mählck, P. and Persson, O. (2000). Socio-bibliometric mapping of intra-departmental networks. Scientometrics 49, 81–91. DOI:10.1023/A:1005661208810.
  • Melin, G. and Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics 36, 363–377.
  • Mena-Chalco, J. P., Digiampietri, L. A. and Cesar Jr., R. M. (2012). Caracterizando as redes de coautoria de currículos Lattes. In Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2012).
  • Mena-Chalco, J. P. and Cesar Jr., R. M. (2009). scriptLattes: An open-source knowledge extraction system from the lattes platform. Journal of the Brazilian Computer Society 15, 31–39.
  • Mena-Chalco, J. P., Digiampietri, L. A., Lopes, F. M. and Cesar, R. M. (2014). Brazilian bibliometric coauthorship networks. Journal of the Association for Information Science and Technology 65, 1424–1445.
  • Milgram, S. (1967). The small world problem. Psychology Today 2, 60–67.
  • Nascimento, S. and Beuren, I. M. (2011). Redes sociais na produção científica dos programas de pós-graduação de ciências contábeis do Brasil. Revista de Administração Contemporânea 15, 47–66.
  • Neal, Z. (2014). The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors. Social Networks 39, 84–97.
  • Newman, M. E. J. (2001). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 64, 016131.
  • Newman, M. E. J. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 69, 026113.
  • Peron, T. K. D., Costa, L. d. F. and Rodrigues, F. A. (2012). The structure and resilience of financial market networks. Chaos 22, 013117.
  • Said, Y. H., Wegman, E. J. and Sharabati, W. K. (2010). Author-coauthor social networks and emerging scientific subfields. In Data Analysis and Classification: From Exploration to Confirmation. Stud. Classification Data Anal. Knowledge Organ. 257–268. Berlin: Springer.
  • Senra, N. (2008). Pesquisa histórica das estatísticas: Temas e fontes. História, Ciências, Saúde 15, 411–425.
  • Senra, N. (2009). Na Primeira República, Bulhões Carvalho legaliza a atividade estatística e a põe na ordem do Estado. Boletim do Museu Paraense Emilio Goeldi. Ciências Humanas 4, 387–399. DOI:10.1590/S1981-81222009000300003.
  • Stefano, D. D., Fuccella, V., Vitale, M. P. and Zaccarin, S. (2013). The use of different data sources in the analysis of co-authorship networks and scientific performance. Social Networks 35, 370–381.
  • Travers, J. and Milgram, S. (1969). An experimental study of the small world problem. Sociometry 32, 425–443. DOI:10.2307/2786545.
  • Wasserman, S. and Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge, MA: Cambridge University Press.
  • Yoshikane, F. and Kageura, K. (2004). Comparative analysis of coauthorship networks of different domains: The growth and change of networks. Scientometrics 60, 435–446.
  • Yousefi-Nooraie, R., Akbari-Kamrani, M., Hanneman, R. A. and Etemadi, A. (2008). Association between co-authorship network and scientific productivity and impact indicators in academic medical research centers: A case study in Iran. Health Research Policy and Systems 6, 1–8.