Abstract
In a co-authorship network papers written together represent the edges, and the authors represent the nodes. By using the concepts of social network analysis, it is possible to better understand the relationship between these nodes. The following question arises: “How does the evolution of the network occur over time?”. To answer this question, it is necessary to understand how two nodes interact with one another, that is, what factors are essential for a new connection to be created. The purpose of this paper is to predict connections in co-authorship networks formed by doctors with resumes registered in the Lattes Platform in the area of Information Sciences. To this end, the following steps are performed: initially the data is extracted, later the co-authorship networks are created, then the attributes to be used are defined and calculated, finally the prediction is performed. Currently, the Lattes Platform has 6.1 million resumes from researchers and represents one of the most relevant and recognized scientific repositories worldwide. Through this study, it is possible to understand which attributes of the nodes make them closer to each other, and therefore have a greater chance of creating a connection between them in the future. This work is extremely relevant because it uses a data set that has been little used in previous studies. Through the results it will be possible to establish the evolution of the network of scientific collaborations of researchers at national level, thus helping the development agencies in the selection of future outstanding researchers.
Supported by CAPES.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
National Council for Scientific and Technological Development.
References
Acar, E., Dunlavy, D.M., Kolda, T.G.: Link prediction on evolving data using matrix and tensor factorizations. In: ICDMW 2009. IEEE International Conference on Data Mining Workshops, 2009, pp. 262–269. IEEE (2009)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Al Hasan, M., Zaki, M.J.: A survey of link prediction in social networks. In: Social Network Data Analytics, pp. 243–275. Springer (2011). https://doi.org/10.1007/978-1-4419-8462-3_9
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Dias, T.M.R., Moita, G.F.: A method for the identification of collaboration in large scientific databases. Em Questão 21(2), 140–161 (2015)
Dias, T.M.R., Moita, G.F., Dias, P.M., Moreira, T.H.J.: Identificação e caracterização de redes científicas de dados currículares. iSys-Revista Brasileira de Sistemas de Informação 7(3), 5–18 (2014)
Dias, T.M., Moita, G.F., Dias, P.M., Moreira, T., Santos, L.: Modelagem e caracterização de redes científicas: um estudo sobre a plataforma lattes. In: BRASNAM-II Brazilian Workshop on Social Network Analysis and Mining, pp. 10–20 (2013)
Dias, T.: Um estudo da produção científica brasileira a partir de dados da plataforma lattes. 181p. Programa de Pós-Graduação em Modelagem Matemática e Computacional, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte (Doutorado) (2016)
Digiampietri, L., Maruyama, W.T., Santiago, C., da Silva Lima, J.J.: Um sistema de predição de relacionamentos em redes sociais. In: Brazilian Symposium on Information Systems, vol. 11 (2015)
Krebs, V.E.: Mapping networks of terrorist cells. Connections 24(3), 43–52 (2002)
Lane, J.: Let’s make science metrics more scientific. Nature 464(7288), 488 (2010)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. In: Conference on Information and Knowledge Management (CIKM 2003), pp. 556–559 (2003)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Liu, Z., Zhang, Q.M., Lü, L., Zhou, T.: Link prediction in complex networks: a local Naïve Bayes model. EPL (Europhys. Lett.) 96(4), 48007 (2011)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys.: Stat. Mech. Appl. 390(6), 1150–1170 (2011)
Maruyama, W.T., Digiampietri, L.A.: Co-authorship prediction in academic social network. In: Anais do V Workshop Brasileiro de Análise de Redes Sociais e Mineração, pp. 79–90. SBC (2019)
Mena-Chalco, J.P., Junior, R.M.C.: Scriptlattes: an open-source knowledge extraction system from the lattes platform. J. Braz. Comput. Soc. 15(4), 31–39 (2009)
Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6912, pp. 437–452. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23783-6_28
Newman, M.E.: The structure of scientific collaboration networks. Proc. Nat. Acad. Sci. 98(2), 404–409 (2001)
Newman, M.E.: Mixing patterns in networks. Phys. Rev. E 67(2), 026126 (2003)
Newman, M.E.: Coauthorship networks and patterns of scientific collaboration. Proc. Nat. Acad. Sci. 101(suppl 1), 5200–5205 (2004)
Newman, M.E., Park, J.: Why social networks are different from other types of networks. Phys. Rev. E 68(3), 036122 (2003)
Newman, M.: Networks: An introduction. Oxford University Press, Oxford (2010)
Perez Cervantes, E.: Análise de redes de colaboração científica: uma abordagem baseada em grafos relacionais com atributos. Ph.D. thesis, Universidade de São Paulo (2015)
Perez-Cervantes, E., Mena-Chalco, J.P., De Oliveira, M.C.F., Cesar, R.M.: Using link prediction to estimate the collaborative influence of researchers. In: 2013 IEEE 9th International Conference on eScience (eScience), pp. 293–300. IEEE (2013)
Potgieter, A., April, K.A., Cooke, R.J., Osunmakinde, I.O.: Temporality in link prediction: understanding social complexity. Emerg.: Complex. Organ. (E: CO) 11(1), 69–83 (2009)
Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Affonso, F., Dias, T.M.R., de Oliveira Santiago, M. (2020). A Strategy for Co-authorship Recommendation: Analysis Using Scientific Data Repositories. In: Mugnaini, R. (eds) Data and Information in Online Environments. DIONE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-50072-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-50072-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50071-9
Online ISBN: 978-3-030-50072-6
eBook Packages: Computer ScienceComputer Science (R0)