A general model of hierarchical fractal scale-free networks
Kousuke Yakubo, Yuka Fujiki

TL;DR
This paper introduces a versatile hierarchical fractal scale-free network model that allows systematic control of structural properties and analyzes their effects on network robustness and percolation phenomena.
Contribution
The authors present a general, adjustable model for fractal scale-free networks, enabling systematic study of structural influences on network behavior and robustness.
Findings
Derived formulas for degree distribution and fractal dimension.
Analyzed the impact of clustering on network robustness.
Extended the model to non-deterministic networks.
Abstract
We propose a general model of unweighted and undirected networks having the scale-free property and fractal nature. Unlike the existing models of fractal scale-free networks (FSFNs), the present model can systematically and widely change the network structure. In this model, an FSFN is iteratively formed by replacing each edge in the previous generation network with a small graph called a generator. The choice of generators enables us to control the scale-free property, fractality, and other structural properties of hierarchical FSFNs. We calculate theoretically various characteristic quantities of networks, such as the exponent of the power-law degree distribution, fractal dimension, average clustering coefficient, global clustering coefficient, and joint probability describing the nearest-neighbor degree correlation. As an example of analyses of phenomena occurring on FSFNs, we also…
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