Identification of the academic genealogy graph of researchers: An approach based on the Lattes Platform

• Rafael J. P. Damaceno Federal University of ABC
• Luciano Rossi Federal University of ABC
• Jesús P. Mena-Chalco Federal University of ABC

DOI: https://doi.org/10.5753/sbbd.2017.171363

Abstract

Different academic-scientific knowledge areas have made efforts to create databases related to researchers as well as the advisor-advisee relationships. However, most of these databases present problems such as redundancy, absence, and imprecision of information. In this paper, we present an algorithm for the automatic identification of academic genealogical graph. The contribution of our work relies on the precision of the hierarchical structure of academic advisor-advisee relationships resulting from the algorithm (graph), which facilitates genealogical analysis. As a case study, we prospected more than 272 thousand Ph.D. researchers registered in the Lattes Platform. Novel characteristics on the Brazilian academic genealogy are discussed in detail.

Keywords: Automatic Identification, Genealogical Analysis, Graphs, Lattes Platform

References

David, S. V. and Hayden, B. Y. (2012). Neurotree: A collaborative, graphical database of the academic genealogy of neuroscience. PloS one, 7(10):e46608.

Dores, W., Benevenuto, F., and Laender, A. H. F. (2016). Extracting academic genealogy trees from the networked digital library of theses and dissertations. In Digital Libraries, 2016 IEEE/ACM Joint Conference on, pages 163–166.

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

Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3):37.

Ferreira, A. A., Gonçalves, M. A., and Laender, A. H. F. (2014). Disambiguating author names using minimum bibliographic information. World Digital Libraries, 7(1):71.

Gargiulo, F., Caen, A., and Carletti, R. L. T. (2016). The classical origin of modern mathematics. EPJ Data Science, 5(1):26.

Kim, J., Diesner, J., Aleyasen, A., Heejun, K., and Hwan-Min, K. (2014). Why name ambiguity resolution matters for scholarly big data research. In 2014 IEEE International Conference on Big Data, pages 1–6.

Korfhage, R. R. (1997). Information Storage and Retrieval. Wiley, 1st edition.

Lane, J. (2010). Let’s make science metrics more scientific. Nature, 464(7288):488–489.

Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and reversals. In Soviet physics doklady, volume 10, page 707.

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

Mena-Chalco, J. P. and Cesar Junior, R. (2013). Prospecção de dados acadêmicos de currículos lattes através de scriptlattes. In Bibliometria e Cientometria: reflexões teóricas e interfaces, pages 109–128.

Rahm, E. and Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4):3–13.

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

Rossi, L. and 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 (BraSNAM), pages 1–12, Brasília, DF, Brazil.

Sugimoto, C. R. (2014). Academic genealogy. In Cronin, B. and Sugimoto, C. R., editors, Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact, pages 365–382. first edition.

Sugimoto, C. R., Ni, C., Russell, T. G., and 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.