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.