Majority-vote model on Opinion-Dependent Networks

TL;DR

This study investigates the critical behavior of the majority-vote model on opinion-dependent networks, revealing it belongs to a distinct universality class from the Ising model on the same networks through Monte Carlo simulations.

Contribution

The paper provides the first detailed finite-size scaling analysis of the majority-vote model on opinion-dependent networks, identifying its critical exponents and universality class.

Findings

Critical exponents: β/ν=0.230(3), γ/ν=0.535(2), 1/ν=0.475(8)

Critical noise parameter: q_c=0.166(3)

Majority-vote model belongs to a different universality class than the equilibrium Ising model on these networks.

Abstract

We study a nonequilibrium model with up-down symmetry and a noise parameter $q$ known as majority-vote model of M.J. Oliveira $1992$ on opinion-dependent network or Stauffer-Hohnisch-Pittnauer networks. By Monte Carlo simulations and finite-size scaling relations the critical exponents $\beta/\nu$, $\gamma/\nu$, and $1/\nu$ and points $q_{c}$ and $U^*$ are obtained. After extensive simulations, we obtain $\beta/\nu=0.230(3)$, $\gamma/\nu=0.535(2)$, and $1/\nu=0.475(8)$. The calculated values of the critical noise parameter and Binder cumulant are $q_{c}=0.166(3)$ and $U^*=0.288(3)$. Within the error bars, the exponents obey the relation $2\beta/\nu+\gamma/\nu=1$ and the results presented here demonstrate that the majority-vote model belongs to a different universality class than the equilibrium Ising model on Stauffer-Hohnisch-Pittnauer networks, but to the same class as majority-vote models on some other networks.