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Condensed Matter > Statistical Mechanics

Title: Ensemble nonequivalence and Bose-Einstein condensation in weighted networks

Abstract: The asymptotic (non)equivalence of canonical and microcanonical ensembles, describing systems with soft and hard constraints respectively, is a central concept in statistical physics. Traditionally, the breakdown of ensemble equivalence (EE) has been associated with nonvanishing relative canonical fluctuations of the constraints in the thermodynamic limit. Recently, it has been reformulated in terms of a nonvanishing relative entropy density between microcanonical and canonical probabilities. The earliest observations of EE violation required phase transitions or long-range interactions. More recent research on binary networks found that an extensive number of local constraints can also break EE, even in absence of phase transitions. Here we study for the first time ensemble nonequivalence in weighted networks with local constraints. Unlike their binary counterparts, these networks can undergo a form of Bose-Einstein condensation (BEC) producing a core-periphery structure where a finite fraction of the link weights concentrates in the core. This phenomenon creates a unique setting where local constraints coexist with a phase transition. We find surviving relative fluctuations only in the condensed phase, as in more traditional BEC settings. However, we also find a non-vanishing relative entropy density for all temperatures, signalling a breakdown of EE due to the presence of an extensive number of constraints, irrespective of BEC. Therefore, in presence of extensively many local constraints, vanishing relative fluctuations no longer guarantee EE.
Subjects: Statistical Mechanics (cond-mat.stat-mech); Disordered Systems and Neural Networks (cond-mat.dis-nn); Mathematical Physics (math-ph); Probability (math.PR)
Journal reference: Chaos, Solitons and Fractals 172 (2023) 113546
DOI: 10.1016/j.chaos.2023.113546
Cite as: arXiv:2012.09998 [cond-mat.stat-mech]
  (or arXiv:2012.09998v2 [cond-mat.stat-mech] for this version)

Submission history

From: Diego Garlaschelli [view email]
[v1] Fri, 18 Dec 2020 00:55:27 GMT (434kb,D)
[v2] Mon, 2 Aug 2021 19:52:26 GMT (443kb,D)

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