Estudo comparativo de métricas de ranking em Redes Sociais

  • Samuel O. Silva IFMT
  • Bruno O. Goulart UEMT
  • Maria Júlia M. Schettini CEFET-MG
  • Carolina Xavier UFSJ
  • João Gabriel Silva IFMT


The use of modeling and application of complex networks in several areas of knowledge have become an important tool for understanding different phenomena; among them some related to the structures and dissemination of information on social medias. In this sense, the use of a network's vertex ranking can be applied in the detection of influential nodes and possible foci of information diffusion. However, calculating the position of the vertices in some of these rankings may require a high computational cost. This paper presents a comparative study between six ranking metrics applied in different social medias. This comparison is made using the rank correlation coefficients. In addition, a study is presented on the computational time spent by each ranking. Results show that the Grau ranking metric has a greater correlation with other metrics and has low computational cost in its execution, making it an efficient indication in detecting influential nodes when there is a short term for the development of this activity.

Palavras-chave: Redes Complexas, Grafos, Métricas de Ranqueamento, Correlações de Rankings, Redes Sociais


Albert, R. and Barabasi, A.-L. (2002). Statistical mechanics of complex networks. Rev. Mod. Phys., 74(1):47–97.

Barbosa, L. M., Attux, R., and Godoy, A. Uma analise de assortatividade e similaridade para artigos científicos.

Cimini, G., Squartini, T., Saracco, F., Garlaschelli, D., Gabrielli, A., and Caldarelli, G. (2019). The statistical physics of real-world networks. Nature Reviews Physics, 1(1):58–71.

Donner, R. V., Lindner, M., Tupikina, L., and Molkenthin, N. (2019). Characterizing flows by complex network methods. In A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems, pages 197–226. Springer.

Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, pages 35–41.

Gabardo, A. C. (2015). Analise de redes sociais: uma visão computacional. Novatec Editora.

Ghent, A. (1963). Kendall’s “tau” coefficient as an index of similarity in comparisons of plant or animal communities. The Canadian Entomologist, 95(06):568–575.

Kleinberg, J. M. (1999a). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604–632.

Kleinberg, J. M. (1999b). Hubs, authorities, and communities. ACM Computing Surveys, 31(4):5.

Kunegis, J. (2013). Konect: the koblenz network collection. In Proceedings of the 22nd International Conference on World Wide Web, pages 1343–1350.

Leskovec, J. and Krevl, A. (2016). Snap datasets: Stanford large network dataset collection (2014). URL http://snap. stanford. edu/data, page 49.

Liben-Nowell, D. and Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American society for information science and technology, 58(7):1019–1031.

Machado, J. R. and Tijiboy, A. V. (2005). Redes sociais virtuais: um espaço paraefetvação da aprendizagem cooperativa. RENOTE-Revista Novas Tecnologias na Educação, 3(1).

Metz, J., Calvo, R., Seno, E. R., Romero, R. A. F., Liang, Z., et al. (2007). Redes complexas: conceitos e aplicações.

Mo, H. and Deng, Y. (2019). Identifying node importance based on evidence theory in complex networks. Physica A: Statistical Mechanics and its Applications, 529:121538.

Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: bringing order to the web.

Rubinov, M. and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3):1059–1069.

Silva, J. G. R., Xavier, C. R., da Fonseca Vieira, V., and de Carvalho, I. A. (2015). Estudo comparativo de metricas de ranqueamento em redes complexas utilizando coeficiente de correlação Congresso Brasileiro de Inteligencia Computacional.

Spearman, C. (1910). Correlation calculated from faulty data. British Journal of Psychology, 3(3):271–295.

Stephenson, K. and Zelen, M. (1989). Rethinking centrality: Methods and examples. Social Networks, 11(1):1–37.

Wasserman, S., Faust, K., et al. (1994). Social network analysis: Methods and applications, volume 8. Cambridge university press.
Como Citar

Selecione um Formato
SILVA, Samuel O.; GOULART, Bruno O. ; SCHETTINI, Maria Júlia M. ; XAVIER, Carolina; SILVA, João Gabriel. Estudo comparativo de métricas de ranking em Redes Sociais. In: ENCONTRO NACIONAL DE COMPUTAÇÃO DOS INSTITUTOS FEDERAIS (ENCOMPIF), 7. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 53-60. ISSN 2763-8766. DOI: