Literature DB >> 25353530

Inverse transitions in a spin-glass model on a scale-free network.

Do-Hyun Kim1.   

Abstract

In this paper, we will investigate critical phenomena by considering a model spin glass on scale-free networks. For this purpose, we consider the Ghatak-Sherrington (GS) model, a spin-1 spin-glass model with a crystal field, instead of the usual Ising-type model. Scale-free networks on which the GS model is placed are constructed from the static model, in which the number of vertices is fixed from the beginning. On the basis of the replica-symmetric solution, we obtain the analytical solutions, i.e., free energy and order parameters, and we derive the various phase diagrams consisting of the paramagnetic, ferromagnetic, and spin-glass phases as functions of temperature T, the degree exponent λ, the mean degree K, and the fraction of the ferromagnetic interactions ρ. Since the present model is based on the GS model, which considers the three states (S = 0, ± 1), the S = 0 state plays a crucial role in the λ-dependent critical behavior: glass transition temperature T(g) has a finite value, even when 2 < λ < 3. In addition, when the crystal field becomes nonzero, the present model clearly exhibits three types of inverse transitions, which occur when an ordered phase is more entropic than a disordered one.

Entities:  

Year:  2014        PMID: 25353530     DOI: 10.1103/PhysRevE.89.022803

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  1 in total

1.  Enhanced storage capacity with errors in scale-free Hopfield neural networks: An analytical study.

Authors:  Do-Hyun Kim; Jinha Park; Byungnam Kahng
Journal:  PLoS One       Date:  2017-10-27       Impact factor: 3.240

  1 in total

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