Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos

TL;DR

This paper introduces Fractal Gaussian Networks, a new sparse random graph model based on Gaussian Multiplicative Chaos, capturing fractal structures in networks and enabling analysis of their properties and detection methods.

Contribution

It presents a novel fractal network model that interpolates between known random geometric graphs and fractal structures, with analytical characterizations and applications to real data.

Findings

Expected number of edges, triangles, and cliques characterized

Detection and parameter estimation methods developed

Application to real-world network data demonstrated

Abstract

We propose a novel stochastic network model, called Fractal Gaussian Network (FGN), that embodies well-defined and analytically tractable fractal structures. Such fractal structures have been empirically observed in diverse applications. FGNs interpolate continuously between the popular purely random geometric graphs (a.k.a. the Poisson Boolean network), and random graphs with increasingly fractal behavior. In fact, they form a parametric family of sparse random geometric graphs that are parametrized by a fractality parameter which governs the strength of the fractal structure. FGNs are driven by the latent spatial geometry of Gaussian Multiplicative Chaos (GMC), a canonical model of fractality in its own right. We asymptotically characterize the expected number of edges, triangles, cliques and hub-and-spoke motifs in FGNs, unveiling a distinct pattern in their scaling with the size parameter of the network. We then examine the natural question of detecting the presence of fractality and the problem of parameter estimation based on observed network data, in addition to fundamental properties of the FGN as a random graph model. We also explore fractality in community structures by unveiling a natural stochastic block model in the setting of FGNs. Finally, we substantiate our results with phenomenological analysis of the FGN in the context of available scientific literature for fractality in networks, including applications to real-world massive network data.