Voronoi and Fractal Complex Networks and Their Characterization

TL;DR

This paper introduces a novel approach to characterizing complex networks using Voronoi tessellations and fractal analysis, linking spatial properties with network topology to better understand various network models.

Contribution

It presents a new method for analyzing complex networks through Voronoi-based spatial metrics and fractal dimensions, applicable to different network types.

Findings

Voronoi tessellations effectively relate networks to metric spaces.

Fractal dimensions reveal scale-invariant properties of networks.

Method distinguishes between random, small-world, scale-free, and fractal networks.

Abstract

Real complex networks are often characterized by spatial constraints such as the relative position and adjacency of nodes. The present work describes how Voronoi tessellations of the space where the network is embedded provide not only a natural means for relating such networks with metric spaces, but also a natural means for obtaining fractal complex networks. A series of comprehensive measurements closely related to spatial aspects of these networks is proposed, which includes the effective length, adjacency, as well as the fractal dimension of the network in terms of the spatial scales defined by successive shortest paths starting from a specific node. The potential of such features is illustrated with respect to the random, small-world, scale-free and fractal network models.