English

Introducing Small-World Network Effect to Critical Dynamics

Disordered Systems and Neural Networks 2009-11-07 v1 Statistical Mechanics

Abstract

We analytically investigate the kinetic Gaussian model and the one-dimensional kinetic Ising model on two typical small-world networks (SWN), the adding-type and the rewiring-type. The general approaches and some basic equations are systematically formulated. The rigorous investigation of the Glauber-type kinetic Gaussian model shows the mean-field-like global influence on the dynamic evolution of the individual spins. Accordingly a simplified method is presented and tested, and believed to be a good choice for the mean-field transition widely (in fact, without exception so far) observed on SWN. It yields the evolving equation of the Kawasaki-type Gaussian model. In the one-dimensional Ising model, the p-dependence of the critical point is analytically obtained and the inexistence of such a threshold p_c, for a finite temperature transition, is confirmed. The static critical exponents, gamma and beta are in accordance with the results of the recent Monte Carlo simulations, and also with the mean-field critical behavior of the system. We also prove that the SWN effect does not change the dynamic critical exponent, z=2, for this model. The observed influence of the long-range randomness on the critical point indicates two obviously different hidden mechanisms.

Keywords

Cite

@article{arxiv.cond-mat/0212542,
  title  = {Introducing Small-World Network Effect to Critical Dynamics},
  author = {Jian-Yang Zhu and Han Zhu},
  journal= {arXiv preprint arXiv:cond-mat/0212542},
  year   = {2009}
}

Comments

30 pages, 1 ps figures, REVTEX, accepted for publication in Phys. Rev. E