Covariance structure behind breaking of ensemble equivalence in random graphs
Diego Garlaschelli, Frank den Hollander, Andrea Roccaverde

TL;DR
This paper investigates the covariance structure underlying the breaking of ensemble equivalence in random graphs with fixed degree sequences, validating a recent conjecture and analyzing different regimes of graph density.
Contribution
It confirms a conjectured formula for relative entropy in random graphs, linking it to covariance determinants and degree distributions across regimes.
Findings
The formula accurately predicts relative entropy in dense regimes.
An extra correction term is needed in sparse and ultra-dense regimes.
Degrees follow a multivariate Poisson-Binomial distribution in the canonical ensemble.
Abstract
For a random graph subject to a topological constraint, the microcanonical ensemble requires the constraint to be met by every realisation of the graph (`hard constraint'), while the canonical ensemble requires the constraint to be met only on average (`soft constraint'). It is known that breaking of ensemble equivalence may occur when the size of the graph tends to infinity, signalled by a non-zero specific relative entropy of the two ensembles. In this paper we analyse a formula for the relative entropy of generic discrete random structures recently conjectured by Squartini and Garlaschelli. We consider the case of a random graph with a given degree sequence (configuration model), and show that in the dense regime this formula correctly predicts that the specific relative entropy is determined by the scaling of the determinant of the matrix of canonical covariances of the constraints.…
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