Skip to main content
Log in

The Brazilian academic genealogy: evidence of advisor–advisee relationships through quantitative analysis

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Science can be examined from several standpoints, such as through a bibliometric analysis of the scientific output of researchers, research groups or institutions. However, there is little information about the advisor–advisee relationships or the academic supervision of researchers or between teachers and students. In this paper, we examine the results of the academic genealogy of PhD and Master’s students working in Brazil, which was obtained from 737,919 curriculum vitae extracted from the Lattes Platform. Our findings bring to light three main sources of evidence related to the Brazilian academic genealogy: (1) the degree of interdisciplinarity between main areas of knowledge, (2) the structural features and evolving patterns with regard to both areas of knowledge and researchers, and (3) the patterns in the levels of training that affect the topological metrics. We conclude that academic genealogy offers a great opportunity to assess researchers and their areas of research from the perspective of human resource training.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Bennett, A. F., & Lowe, C. (2005). The academic genealogy of George A. Bartholomew. Integrative and Comparative Biology, 45(2), 231–233.

    Article  Google Scholar 

  • Center for Strategic Studies and Management Science, Technology and Innovation. (2016). Doutores 2015: Estudos da demografia da base técnico-científica brasileira. Brasília, DF: Centro de Gestão e Estudos Estratégicos.

    Google Scholar 

  • Chariker, J. H., Zhang, Y., Pani, J. R., & Rouchka, E. C. (2017). Identification of successful mentoring communities using network-based analysis of mentor–mentee relationships across nobel laureates. Scientometrics, 111(3), 1733–1749.

    Article  Google Scholar 

  • Damaceno, R. J. P., Rossi, L., & Mena-Chalco, J. P. (2017). Identificação do grafo de genealogia acadêmica de pesquisadores: Uma abordagem baseada na plataforma Lattes. In Proceedings of the 32nd Brazilian symposium on databases (pp. 76–87).

  • David, S. V., & Hayden, B. Y. (2012). Neurotree: A collaborative, graphical database of the academic genealogy of neuroscience. PLoS ONE, 7(10), e46608.

    Article  Google Scholar 

  • Dores, W., Soares, E., Benevenuto, F., & Laender, A. H. F. (2017). Building the Brazilian academic genealogy tree. In J. Kamps, G. Tsakonas, Y. Manolopoulos, L. Iliadis, & I. Karydis (Eds.), Research and advanced technology for digital libraries (pp. 537–543). Berlin: Springer.

    Chapter  Google Scholar 

  • Elias, M., Floeter-Winter, L. M., & Mena-Chalco, J. P. (2016). The dynamics of Brazilian protozoology over the past century. Memórias do Instituto Oswaldo Cruz, 111(1), 67–74.

    Article  Google Scholar 

  • Heinisch, D. P., & Buenstorf, G. (2018). The next generation (plus one): An analysis of doctoral students’ academic fecundity based on a novel approach to advisor identification. Scientometrics, 177, 351–380.

    Article  Google Scholar 

  • Jackson, A. (2007). A labor of love: The mathematics genealogy project. Notices of the American Mathematical Society, 54(8), 1002–1003.

    Google Scholar 

  • Kelley, E. A., & Sussman, R. W. (2007). An academic genealogy on the history of American field primatologists. American Journal of Physical Anthropology, 132(3), 406–425.

    Article  Google Scholar 

  • Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10, 707.

    MathSciNet  Google Scholar 

  • Li, Y., Fang, N., Liu, Z., & Yu, H. (2017). Inferring advisor–student relationships from publication networks based on approximate maxconfidence measure. Mathematical Problems in Engineering. https://doi.org/10.1155/2017/8135464.

    Google Scholar 

  • Liu, J., Tang, T., Kong, X., Tolba, A., AL-Makhadmeh, Z., & Xia, F. (2018). Understanding the advisor–advisee relationship via scholarly data analysis. Scientometrics, 116(1), 161–180.

    Article  Google Scholar 

  • Malmgren, R. D., Ottino, J. M., & Amaral, L. A. N. (2010). The role of mentorship in protégé performance. Nature, 465(7298), 622–626.

    Article  Google Scholar 

  • Martin, S., Brown, W. M., Klavans, R., & Boyack, K. W. (2011). Openord: An open-source toolbox for large graph layout. In SPIE proceedings—Visualization and data analysis 2011 (Vol. 7868, pp. 786–806). International Society for Optics and Photonics.

  • Mena-Chalco, J. P., Digiampietri, L. A., Lopes, F. M., & Cesar Jr., R. M. (2014). Brazilian bibliometric coauthorship networks. Journal of the Association for Information Science and Technology, 65(7), 1424–1445.

    Article  Google Scholar 

  • Montoye, H. J., & Washburn, R. (1980). Research quarterly contributors: An academic genealogy. Research Quarterly for Exercise and Sport, 51(1), 261–266.

    Article  Google Scholar 

  • Rossi, L., Damaceno, R. J. P., Freire, I. L., Bechara, E. J. H., & Mena-Chalco, J. P. (2018). Topological metrics in academic genealogy graphs. Journal of Informetrics, 12(4), 1042–1058.

    Article  Google Scholar 

  • Rossi, L., Freire, I. L., & Mena-Chalco, J. P. (2017). Genealogical index: A metric to analyze advisor–advisee relationships. Journal of Informetrics, 11(2), 564–582.

    Article  Google Scholar 

  • Rossi, L., & Mena-Chalco, J. P. (2014). Caracterização de árvores de genealogia acadêmica por meio de métricas em grafos. In Brazilian workshop on social network analysis and mining (pp. 1–12).

  • Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41(1), 643–681.

    Article  Google Scholar 

  • Sugimoto, C. R. (2014). Academic genealogy. In B. Cronin & C. R. Sugimoto (Eds.), Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact (first ed., pp. 365–382). Cambridge: MIT Press.

    Google Scholar 

  • Sugimoto, C. R., Ni, C., Russell, T. G., & Bychowski, B. (2011). Academic genealogy as an indicator of interdisciplinarity: An examination of dissertation networks in library and information science. Journal of the American Society for Information Science and Technology, 62(9), 1808–1828.

    Article  Google Scholar 

  • Tuesta, E. F., Delgado, K. V., Mugnaini, R., Digiampietri, L. A., Mena-Chalco, J. P., & Pérez-Alcázar, J. J. (2015). Analysis of an advisor–advisee relationship: An exploratory study of the area of exact and earth sciences in Brazil. PLoS ONE, 10(5), e0129065.

    Article  Google Scholar 

  • Vanz, A. S. S., & Stumpf, I. R. C. (2010). Colaboração científica: revisão teórico-conceitual. Perspectivas em Ciência da Informação, 15(2), 42–55.

    Article  Google Scholar 

  • Wang, C., Han, J., Jia, Y., Tang, J., Zhang, D., Yu, Y., & Guo, J. (2010). Mining advisor–advisee relationships from research publication networks. In Proceedings of the 16th international conference on knowledge discovery and data mining (pp. 203–212). ACM.

  • Wang, W., Liu, J., Xia, F., King, I., & Tong, H. (2017). Shifu: Deep learning based advisor–advisee relationship mining in scholarly big data. In Proceedings of the 26th international conference on World Wide Web companion (pp. 303–310). International World Wide Web Conferences Steering Committee.

Download references

Acknowledgements

The authors would like to thank the Federal University of ABC for its financial support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafael J. P. Damaceno.

Appendix

Appendix

We investigated the influence between main areas of knowledge in both PhD and MSc & PhD graphs. Figure 9 shows 16 radar charts (two by main area of knowledge). The axis of the radar charts uses the logarithmic function applied to the data shown in Table 4. As observed in Table 4, main areas of knowledge have a certain degree of interdisciplinarity. Moreover, while comparing the two graphs, there is a similar pattern in the influence exerted and experienced for both graphs PhD and MSc & PhD.

We also analyzed who are the fifteen artificial nodes with the highest values for each of the five metrics. Table 7 presents the values for the metrics descendants and fecundity, as well as the genealogical index for the 15 researchers without Lattes ID with the highest values, for both PhD and MSc & PhD graphs.

Fig. 9
figure 9

Influence exerted (blue) and experienced (red) for each main area of knowledge. The radar charts are in logarithmic scale. (Color figure online)

Table 7 Top 15 values for metrics descendants (\(d^+\) and \(d^-\)), fecundity (\(f^+\) and \(f^-\)) and genealogical index (g) for graphs: (a) PhD, (b) MSc & PhD. Only artificial nodes

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Damaceno, R.J.P., Rossi, L., Mugnaini, R. et al. The Brazilian academic genealogy: evidence of advisor–advisee relationships through quantitative analysis. Scientometrics 119, 303–333 (2019). https://doi.org/10.1007/s11192-019-03023-0

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-019-03023-0

Keywords

Navigation