Estimating Future Collaborations on Data on Scientific Activities

Authors

  • Thiago Magela Rodrigues Dias

DOI:

https://doi.org/10.18225/ci.inf.v49i3.5470

Keywords:

scientific collaboration, link prediction, Lattes Platform

Abstract

In a scientific collaboration network, a connection is formed when two or more scientists publish a work together, in which case, the works represent the edges, and the scientists represent the nodes of the network. Using concepts from the analysis of social networks, it is possible to better understand the relationship between nodes. The work in question aims to make the prediction of connections in co-authorship networks formed by PhDs with curricula registered in the Lattes Platform, and whose area of ​​activity is Information Sciences. Currently, the Lattes Platform has 6.6 million curricula of individuals and represents one of the most relevant and recognized scientific repositories worldwide. With this, it is possible to understand the behavior of the network and monitor its evolution over time. For that, some steps are necessary, they are: data extraction, creation of co-authorship networks, definition of the attributes to be used, creation of a data set, and finally, use them as input in a machine learning algorithm. Through the results it is possible to establish, with precision, the evolution of the network of scientific collaborations of the researchers at national level, thus assisting the funding agencies in the choice of future outstanding researchers.

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Author Biography

  • Thiago Magela Rodrigues Dias

    Doutor em Modelagem Matemática e Computacional pela Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), Belo Horizonte, Minas Gerais, Brasil. Professor do Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG) - Belo Horizonte, MG - Brasil.

     

References

ACAR, E., DUNLAVY, D. M., KOLDA, T. G. Link Prediction on Evolving Data Using Matrix and Tensor Factorizations. Ieee. Data Mining Workshops, 2009. Icdmw’09. Ieee International Conference On, 2009, p. 262–269.
ADAMIC, L. A., ADAR, E. Friends And Neighbors On The Web. Social Networks, Elsevier, 2003, v.25, n. 3, p. 211–230.
BARABÁSI, A.-L. E ALBERT, R. Emergence of scaling in random networks. Science, American Association for the Advancement of Science, 1999, v.286, n.5439, p. 509–512.
CAÑIBANO, C.; BOZEMAN, B. Curriculum vitae method in science policy and re-search evaluation: the state-of-the-art. Research Evaluation, v. 18, n. 2, p. 86-94, 2009.
CHEN, H., LI, X., HUANG, Z. Link Prediction Approach to Collaborative Filtering. Ieee. Proceedings Of The 5th Acm/Ieee-Cs Joint Conference On Digital Libraries (Jcdl’05), 2005, p.141–142.
DIAS, T. M. et al. Modelagem E Caracterização De Redes Científicas: Um Estudo Sobre A Plataforma Lattes. Brasnam-Ii Brazilian Workshop On Social Network Analysis And Mining, 2013. p. 10–20.
DIAS, T. M. R. e MOITA, G. F. A Method For The Identification Of Collaboration In Large Scientific Databases. Em Questão, 2015, Vol. 21, N. 2, p. 140–161.
DIAS, T. Um Estudo Da Produção Científica Brasileira a Partir De Dados Da Plataforma Lattes. Programa De Pós-Graduação Em Modelagem Matemática E Computacional, Cen-tro Federal De Educação Tecnológica De Minas Gerais, Belo Horizonte (Doutorado), 2016, 181p.
DIGIAMPIETRI, L. A; SANTIAGO, C. R. N.; ALVES, C. M. Predição de coautorias em redes sociais acadêmicas: um estudo exploratório em Ciência da Computação. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING, 2, 2013, Anais… Maceió, 2013.
DIGIAMPIETRI, L. et al. Um Sistema De Predição De Relacionamentos Em Redes So-ciais. Brazilian Symposium on Information Systems, 2015, V. 11.
HASAN, M. A. e ZAKI, M. J. A Survey of Link Prediction In Social Networks. Social Network Data Analytics. Springer, 2011, p. 243–275.
LANE, J. Let’s Make Science Metrics More Scientific. Nature, Nature Publishing Group, 2010, v. 464, n. 7288, p. 488.
LIBEN-NOWELL, D. e KLEINBERG, J. The Link-Prediction Problem for Social Net-works. Journal of The American Society For Information Science And Technology, Wiley Online Library, 2007, v.58, n.7, p.1019–1031.
LIMA, H. et al. Aggregating productivity indices for ranking researchers across multiple areas. In: PROCEEDINGS OF THE 13TH ACM/IEEE-CS JOINT CONFERENCE ON DIGITAL LIBRARIES, ACM, p. 97-106, 2013.
LIU, Z. et al. Link Prediction in Complex Networks: A Local Naïve Bayes Model. Epl (Europhysics Letters), Iop Publishing, 2011, v.96, n.4, p.48007.
LÜ, L. e ZHOU, T. Link Prediction in Complex Networks: A Survey. Physica A: Statis-tical Mechanics And Its Applications, Elsevier, 2011, v.390, n.6, p.1150–1170.
MARUYAMA, W. T. e DIGIAMPIETRI, L. A. Co-Authorship Prediction In Academic Social Network. Sbc. Anais Do V Workshop Brasileiro De Análise De Redes Sociais E Mineração, 2019. p.79–90.
MENA-CHALCO, J. P.; CESAR-JUNIOR, R. M. Prospecção de dados acadêmicos de currículos Lattes através de scriptLattes. In: HAYASHI, M. C. P. I.; LETA, H. E. J. (Orgs.). Bibliometria e Cientometria: reflexões teóricas e interfaces. São Carlos: Pedro & João, p. 109-128, 2013.
MENA-CHALCO, J. P.; et al. Brazilian bibliometric coauthorship networks. Journal of the Association for Information Science and Technology, v. 65, n. 7, p. 1424-1445, 2014.
NEWMAN, M. E. The structure of scientific collaboration networks. Proceedings of the national academy of sciences, National Acad Sciences, 2001, v.98, n.2, p. 404–409.
NEWMAN, M. E.; PARK, J. Why social networks are different from other types of net-works. Physical Review E, APS, 2003, v. 68, n. 3, p. 036122.
PEREZ-CERVANTES, E. et al. Using link prediction to estimate the collaborative influ-ence of researchers. In: IEEE 9TH INTERNATIONAL CONFERENCE ON ESCI-ENCE (ESCIENCE), 9. Anais… China, Beijin, p. 293-300, 2013.
POTGIETER, A. et al. Temporality in Link Prediction: Understanding Social Complexi-ty. Emergence. Complexity & Organization (E: Co), Citeseer, 2009, v.11, n.1, p.69–83.
SIDONE, O. J. G.; HADDAD, E. A.; MENA-CHALCO, J. P. A Ciência nas Regiões Brasileiras: Evolução da Produção e das Redes de Colaboração Científica. Transin-formação. v. 28, n. 1, p. 15-31, 2016.
ZHOU, T., LÜ, L., ZHANG, Y.-C. Predicting Missing Links Via Local Information. The European Physical Journal B, Springer, 2009, v.71, n.4, p.623–630.

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Published

25/11/2020

Issue

Vol. 49 No. 3 (2020): Data Science in Information Science

Section

Articles

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Copyright (c) 2020 Thiago Magela Rodrigues Dias

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