A novel approach to estimated Boulingand-Minkowski fractal dimension from complex networks

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.

Automated summary

The fractal dimension is an important feature for characterizing the behavior and dynamics of complex networks. Existing methods for estimating the fractal dimension in complex networks have been proposed, but there is no known adaptation for the Bouligand-Minkowski method. In this study, we propose an adaptation of the Bouligand-Minkowski method for measuring the fractal dimension of complex networks and verify its potential in a classification task.