Breaking of ensemble equivalence for dense random graphs under a single constraint
Frank den Hollander, Maarten Markering

TL;DR
This paper investigates the conditions under which ensemble equivalence breaks down in dense random graphs with a single density constraint, revealing a specific phase where spectral and structural differences emerge.
Contribution
It establishes the occurrence of breaking of ensemble equivalence (BEE) for dense graphs with one constraint, identifying the BEE-phase and spectral signatures, and analyzing the ensembles' asymptotic behaviors.
Findings
BEE occurs in a specific density and edge range for dense graphs.
Spectral gap exists between ensembles in the BEE-phase.
Microcanonical behaves like Erdős-Rényi, canonical like a mixture of two Erdős-Rényi graphs.
Abstract
Two ensembles are often used to model random graphs subject to constraints: the microcanonical ensemble (= hard constraint) and the canonical ensemble (= soft constraint). It is said that breaking of ensemble equivalence (BEE) occurs when the specific relative entropy of the two ensembles does not vanish as the size of the graph tends to infinity. The latter means that it matters for the scaling properties of the graph whether the constraint is met for every single realisation of the graph or only holds as an ensemble average. In the literature, it was found that BEE is the rule rather than the exception for two classes: sparse random graphs when the number of constraints is of the order of the number of vertices and dense random graphs when there are two or more constraints that are frustrated. In the present paper we establish BEE for a third class: dense random graphs with a single…
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Taxonomy
TopicsTopological and Geometric Data Analysis
