Identification of the Brazilian academic roots through mining advisor-advisee relationships
Resumo
This study seek to carry out an identification and analysis of academic roots using academic genealogy graphs as data source. These graphs are used to identify the academic roots of 85 areas of knowledge and analyze the influences prevailing between them. The results show that science in Brazil is young, with most of the PhD and master’s graduates having obtained an academic degree between the years 1980 and 2000. We detected some key areas of knowledge, such as Education and Medicine that exert a considerable influence on the mentoring of academics in several areas of knowledge. The significance of this study is that it employs a method to use mentoring relationships for the identification of the academic roots of areas of knowledge, that could be applied to any academic genealogical graph.
Palavras-chave:
graph mining, academic roots, advisor-advisee relationships
Referências
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Boschma, R., Heimeriks, G., and Balland, P.-A. Scientific knowledge dynamics and relatedness in biotech cities. Research Policy 43 (1): 107–114, 2014.
Chatfield, C. Introduction to multivariate analysis. Routledge, 2018.
Damaceno, R. J. P., Rossi, L., and Mena-Chalco, J. P. Identificação do grafo de genealogia acadêmica de pesquisadores: Uma abordagem baseada na Plataforma Lattes. In Proceedings of the 32th Brazilian Symposium on Databases. SBC, 2017.
Dezfoulian, H., Afrazeh, A., and Karimi, B. A new model to optimize the knowledge exchange in industrial cluster: A case study of semnan plaster production industrial cluster. Scientia Iranica. Transaction E, Industrial Engineering 24 (2): 834, 2017.
Ding, C. G., Hung, W.-C., Lee, M.-C., and Wang, H.-J. Exploring paper characteristics that facilitate the knowledge flow from science to technology. Journal of Informetrics 11 (1): 244–256, 2017.
Gao, X., Chen, Y., Song, W., Peng, X., and Song, X. Regional university-industry knowledge flow: A study of chinese academic patent licensing data. Open Journal of Social Sciences 3 (02): 59, 2015.
Jianyu, Z., Baizhou, L., Xi, X., Guangdong, W., and Tienan, W. Research on the characteristics of evolution in knowledge flow networks of strategic alliance under different resource allocation. Expert Systems with Applications, 2017.
Mohammadi, E. and Thelwall, M. Mendeley readership altmetrics for the social sciences and humanities: Research evaluation and knowledge flows. Journal of the Association for Information Science and Technology 65 (8): 1627–1638, 2014.
Rafols, I. and Meyer, M. Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics 82 (2): 263–287, 2010.
Rinia, E., van Leeuwen, T., Bruins, E., van Vuren, H., and van Raan, A. Measuring knowledge transfer between fields of science. Scientometrics 54 (3): 347–362, 2002.
Sorenson, O., Rivkin, J. W., and Fleming, L. Complexity, networks and knowledge flow. Research policy 35 (7): 994–1017, 2006.
Sugimoto, C. R. Academic genealogy. In Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact, first ed., B. Cronin and C. R. Sugimoto (Eds.). MIT Press, pp. 365–382, 2014.
Boschma, R., Heimeriks, G., and Balland, P.-A. Scientific knowledge dynamics and relatedness in biotech cities. Research Policy 43 (1): 107–114, 2014.
Chatfield, C. Introduction to multivariate analysis. Routledge, 2018.
Damaceno, R. J. P., Rossi, L., and Mena-Chalco, J. P. Identificação do grafo de genealogia acadêmica de pesquisadores: Uma abordagem baseada na Plataforma Lattes. In Proceedings of the 32th Brazilian Symposium on Databases. SBC, 2017.
Dezfoulian, H., Afrazeh, A., and Karimi, B. A new model to optimize the knowledge exchange in industrial cluster: A case study of semnan plaster production industrial cluster. Scientia Iranica. Transaction E, Industrial Engineering 24 (2): 834, 2017.
Ding, C. G., Hung, W.-C., Lee, M.-C., and Wang, H.-J. Exploring paper characteristics that facilitate the knowledge flow from science to technology. Journal of Informetrics 11 (1): 244–256, 2017.
Gao, X., Chen, Y., Song, W., Peng, X., and Song, X. Regional university-industry knowledge flow: A study of chinese academic patent licensing data. Open Journal of Social Sciences 3 (02): 59, 2015.
Jianyu, Z., Baizhou, L., Xi, X., Guangdong, W., and Tienan, W. Research on the characteristics of evolution in knowledge flow networks of strategic alliance under different resource allocation. Expert Systems with Applications, 2017.
Mohammadi, E. and Thelwall, M. Mendeley readership altmetrics for the social sciences and humanities: Research evaluation and knowledge flows. Journal of the Association for Information Science and Technology 65 (8): 1627–1638, 2014.
Rafols, I. and Meyer, M. Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics 82 (2): 263–287, 2010.
Rinia, E., van Leeuwen, T., Bruins, E., van Vuren, H., and van Raan, A. Measuring knowledge transfer between fields of science. Scientometrics 54 (3): 347–362, 2002.
Sorenson, O., Rivkin, J. W., and Fleming, L. Complexity, networks and knowledge flow. Research policy 35 (7): 994–1017, 2006.
Sugimoto, C. R. Academic genealogy. In Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact, first ed., B. Cronin and C. R. Sugimoto (Eds.). MIT Press, pp. 365–382, 2014.
Publicado
22/10/2018
Como Citar
DAMACENO, R. J. P.; ROSSI, L.; MENA-CHALCO, J. P..
Identification of the Brazilian academic roots through mining advisor-advisee relationships. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 6. , 2018, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 97-104.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2018.27390.
