| Full text | |
| Author(s): |
de Sa, Luiz Alberto Pereira
;
Zielinski, Kallil M. C.
;
Rodrigues, Erick Oliveira
;
Backes, Andre R.
;
Florindo, Joao B.
;
Casanova, Dalcimar
Total Authors: 6
|
| Document type: | Journal article |
| Source: | CHAOS SOLITONS & FRACTALS; v. 157, p. 10-pg., 2022-04-01. |
| Abstract | |
A complex network presents many topological features which characterize its behavior and dynamics. This characterization is an essential aspect of complex networks analysis and can be performed using sev-eral measures, including the fractal dimension. Originally the fractal dimension measures the complexity of an object in a Euclidean space, and the most common methods in the literature to estimate that di-mension are box-counting, mass-radius, and Bouligand-Minkowski. However, networks are not Euclidean objects, so that these methods require some adaptation to measure the fractal dimension in this con -text. The literature presents some adaptations for methods like box-counting and mass-radius. However, there is no known adaptation developed for the Bouligand-Minkowski method. In this way, we propose an adaptation of the Bouligand-Minkowski to measure complex networks' fractal dimension. We com-pare our proposed method with others, and we also explore the application of the proposed method in a classification task of complex networks that confirmed its promising potential.(c) 2022 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 20/01984-8 - Introducing elements of fractal geometry into deep convolutional neural networks: an application to the recognition and categorization of Lung Cancer |
| Grantee: | Joao Batista Florindo |
| Support Opportunities: | Regular Research Grants |