Scaling limits of random graph models at criticality: Universality and the basin of attraction of the Erd\H{o}s-R\'enyi random graph
Shankar Bhamidi, Nicolas Broutin, Sanchayan Sen, Xuan Wang

TL;DR
This paper establishes a general framework for proving the universality of the critical component structure in various random graph models, extending known results from Erdős-Rényi graphs to more complex models.
Contribution
It develops a unified approach to prove the convergence of critical components to fractal structures in broad classes of random graphs, including configuration and inhomogeneous models.
Findings
Proved component size scaling as n^{2/3} in new models.
Established structural convergence to fractals at criticality.
Derived new critical and subcritical regime properties for specific models.
Abstract
A wide array of random graph models have been postulated to understand properties of observed networks. Typically these models have a parameter and a critical time when a giant component emerges. It is conjectured that for a large class of models, the nature of this emergence is similar to that of the Erd\H{o}s-R\'enyi random graph, in the sense that (a) the sizes of the maximal components in the critical regime scale like , and (b) the structure of the maximal components at criticality (rescaled by ) converges to random fractals. To date, (a) has been proven for a number of models using different techniques. This paper develops a general program for proving (b) that requires three ingredients: (i) in the critical scaling window, components merge approximately like the multiplicative coalescent, (ii) scaling exponents of susceptibility functions are the same…
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Taxonomy
TopicsComplex Network Analysis Techniques · Stochastic processes and statistical mechanics · Theoretical and Computational Physics
