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

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 ν 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. Stochastic Networks and Fractality The unreasonable effectiveness of stochastic networks. Stochastic networks have emerged as one of the fundamental modeling paradigms in the last few decades in our efforts to effectively understand interactions underlying vast amounts of data with increasing complexity, and in order to capture the effects of latent factors. At a broad level of abstraction, this involves nodes representing agents, and edges (weighted or otherwise) that embody the interactions between these agents. Indeed, the ubiquity of stochastic network models in the modern applied sciences may justifiably remind one of Eugene Wigner's famous article, [Wig60], on the unreasonable effectiveness of mathematics in the natural sciences.