The fractal nature of complex networks has received a great deal of research interest in the last two decades. Similarly to geometric fractals, the fractality of networks can also be defined with the so-called box-covering method. A network is called fractal if the minimum number of boxes needed to cover the entire network follows a power-law relation with the size of the boxes. The fractality of networks has been associated with various network properties throughout the years, for example, disassortativity, repulsion between hubs, long-range-repulsive correlation, and small edge betweenness centralities. However, these assertions are usually based on tailor-made network models and on a small number of real networks, hence their ubiquity is often disputed. Since fractal networks have been shown to have important properties, such as robustness against intentional attacks, it is in dire need to uncover the underlying mechanisms causing fractality. Hence, the main goal of this work is to get a better understanding of the origins of fractality in complex networks. To this end, we systematically review the previous results on the relationship between various network characteristics and fractality. Moreover, we perform a comprehensive analysis of these relations on five network models and a large number of real-world networks originating from six domains. We clarify which characteristics are universally present in fractal networks and which features are just artifacts or coincidences.

翻译：复杂网络的分形性质在过去二十年中引起了广泛的研究兴趣。与几何分形类似，网络的分形性也可以通过所谓的盒覆盖方法来定义。如果一个网络覆盖整个网络所需的最小盒子数量与盒子大小呈幂律关系，则该网络被称为分形网络。多年来，网络的分形性与多种网络特性相关联，例如异配性、枢纽节点间的排斥性、长程排斥相关性以及较小的边介数中心性。然而，这些断言通常基于定制化的网络模型和少量真实网络，因此其普遍性常受到质疑。由于分形网络已被证明具有重要特性，例如对蓄意攻击的鲁棒性，因此迫切需要揭示导致分形性的潜在机制。因此，本研究的主要目标是更好地理解复杂网络中分形性的起源。为此，我们系统回顾了先前关于网络特性与分形性关系的研究成果。此外，我们对五种网络模型以及来自六个领域的大量真实世界网络进行了这些关系的全面分析。我们澄清了哪些特性普遍存在于分形网络中，哪些特征仅仅是人为产物或巧合。