Citation Analysis Disparity Between Sub-Areas of Brazilian Computer Science

• Fernando F. Druszcz UFPR
• André L. Vignatti UFPR

DOI: https://doi.org/10.5753/brasnam.2024.1922

Resumo

Among the various ways of evaluating scientific production, there is a tendency to use metrics based on the number of citations. Apart from obvious problems, this takes on a new dimension when it is used to compare areas and sub-areas, specially from unfair assessments if submitted to the same evaluation committee. In this work, we examine various sub-areas of Computer Science using data from the Brazilian community. Our findings reveal a disparity in citations among these sub-areas, which may lead to issues if they are evaluated using the same criteria for scientific productivity. We demonstrate how the universal fit citation, previously proposed by Radicchi et al., can help mitigated these concerns.

Referências

Albert, R. and Barabási, A.-L. (2002). Statistical mechanics of complex networks. Rev. Mod. Phys., 74:47–97.

Bibliography, D. C. S. (2022). Monthly snapshot. [link]. Accessed 30/11/2022.

Broido, A. D. and Clauset, A. (2019). Scale-free networks are rare. Nature Communications, 10(1017).

Clauset, A., Shalizi, C. R., and Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM Review, 51(4):661–703.

Dong, Y., Ma, H., Shen, Z., and Wang, K. (2017). A century of science: Globalization of scientific collaborations, citations, and innovations. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 1437–1446. Association for Computing Machinery.

Easley, D. and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press.

Golosovsky, M. (2021). Universality of citation distributions: A new understanding. Quantitative Science Studies, 2(2):527–543.

Golosovsky, M. and Larivière, V. (2021). Uncited papers are not useless. Quantitative Science Studies, 2(3):899–911.

Katchanov, Y. L., Markova, Y. V., and Shmatko, N. A. (2023). Uncited papers in the structure of scientific communication. Journal of Informetrics, 17(2):101391.

Lima, A., Vignatti, A., and Silva, M. (2019). Recognizing power-law graphs by machine learning algorithms using a reduced set of structural features. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional, pages 611–621. SBC.

Mitzenmacher, M. and Upfal, E. (2017). Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis. Cambridge University Press.

Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2):167–256.

Newman, M. E. J. (2005). Power laws, pareto distributions and zipf’s law. Contemporary Physics, 46(5):323–351.

Noorden, R. V., Maher, B., and Nuzzo, R. (2014). The top 100 papers. Nature, 514(7524):550–553.

Radicchi, F., Fortunato, S., and Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. In National Academy of Sci.

Ullmann, T. (2012). Co-citation analysis of the topic social network analysis. [link]. Accessed 2023/10/15.

Valente, M. T. and Paixao, K. (2018). CSIndexbr: Exploring the Brazilian scientific production in Computer Science. arXiv, abs/1807.09266.

Waltman, L., Larivière, V., Milojević, S., and Sugimoto, C. R. (2020). Opening science: The rebirth of a scholarly journal. Quantitative Science Studies, 1(1):1–3.

Wang, D. and Barabási, A. (2021). The Science of Science. Cambridge University Press.