# Comparative Analysis of Box-Covering Algorithms for Fractal Networks

arXiv: https://arxiv.org/abs/2105.01939 · HTML: https://mlsanity.com/paper/2105.01939/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2021.

## Built on by (most cited first, 1 of 1)

- [Self-similarity Analysis in Deep Neural Networks](https://mlsanity.com/paper/2507.17785/) (2025)

## Abstract (arXiv)

Research on fractal networks is a dynamically growing field of network science. A central issue is to analyze fractality with the so-called box-covering method. As this problem is known to be NP-hard, a plethora of approximating algorithms have been proposed throughout the years. This study aims to establish a unified framework for comparing approximating box-covering algorithms by collecting, implementing, and evaluating these methods in various aspects including running time and approximation ability. This work might also serve as a reference for both researchers and practitioners, allowing fast selection from a rich collection of box-covering algorithms with a publicly available codebase.