Open AccessEstudo comparativo de métricas de ranking em Redes Sociais
Author(s) -
Samuel O. Silva,
Bruno O. Goulart,
Maria Júlia M. Schettini,
Carolina Ribeiro Xavier,
João Gabriel Silva
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/encompif.2020.11068
Subject(s) - ranking (information retrieval) , metric (unit) , computer science , rank (graph theory) , vertex (graph theory) , correlation , data mining , theoretical computer science , information retrieval , mathematics , combinatorics , engineering , graph , operations management , geometry
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.