Comparative Analysis of Box-Covering Algorithms for Fractal Networks

TL;DR

This paper compares various box-covering algorithms for fractal networks, evaluating their efficiency and accuracy to aid researchers in selecting suitable methods, and provides a unified framework and codebase for future work.

Contribution

It introduces a unified framework for comparing box-covering algorithms, including implementation and evaluation, and offers a publicly available codebase for researchers and practitioners.

Findings

Algorithms vary significantly in running time and approximation quality.

The framework facilitates systematic comparison and selection of algorithms.

Public codebase enhances reproducibility and practical application.

Abstract

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.