Inhomogeneous random graphs with infinite-mean fitness variables
Luca Avena, Diego Garlaschelli, Rajat Subhra Hazra, Margherita Lalli

TL;DR
This paper analyzes an inhomogeneous random graph model with heavy-tailed, infinite-mean vertex variables, characterizing degree distributions, correlations, and subgraph densities, revealing unique asymptotic behaviors.
Contribution
It extends the mathematical understanding of scale-free inhomogeneous random graphs to the case of infinite-mean vertex variables, providing new asymptotic results.
Findings
Vertex degree converges to a mixed Poisson distribution after scaling.
Degrees of different vertices exhibit asymptotic non-vanishing correlations.
Identifies a crossover in the existence of disconnected vertices (dust).
Abstract
We consider an inhomogeneous Erd\H{o}s-R\'enyi random graph ensemble with exponentially decaying random disconnection probabilities determined by an i.i.d. field of variables with heavy tails and infinite mean associated to the vertices of the graph. This model was recently investigated in the physics literature in Garuccio et al. (2020) as a scale-invariant random graph within the context of network renormalization. From a mathematical perspective, the model fits in the class of scale-free inhomogeneous random graphs whose asymptotic geometrical features have been recently attracting interest. While for this type of graphs several results are known when the underlying vertex variables have finite mean and variance, here instead we consider the case of one-sided stable variables with necessarily infinite mean. To simplify our analysis, we assume that the variables are sampled from a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
