Coarse-grained Monte Carlo simulations of the phase transition of Potts model on weighted networks

TL;DR

This paper introduces a strength-based coarse-grained Monte Carlo method for analyzing the phase transition of the Potts model on weighted networks, effectively reducing network size while preserving critical phenomena.

Contribution

The paper presents a novel strength-based coarse-graining approach that maintains statistical consistency and accurately reproduces phase transition properties of the Potts model on weighted networks.

Findings

The G method accurately reproduces phase diagrams and fluctuations.

It outperforms previous G approaches in preserving critical phenomena.

The method is effective on scale-free networks with and without strength-correlation.

Abstract

Developing effective coarse grained (CG) approach is a promising way for studying dynamics on large size networks. In the present work, we have proposed a strength-based CG (\sCG) method to study critical phenomena of the Potts model on weighted complex networks. By merging nodes with close strength together, the original network is reduced to a CG-network with much smaller size, on which the CG-Hamiltonian can be well-defined. In particular, we make error analysis and show that our strength-based CG approach satisfies the condition of statistical consistency, which demands that the equilibrium probability distribution of the CG-model matches that of the microscopic counterpart. Extensive numerical simulations are performed on scale-free networks, without or with strength-correlation, showing that this \sCG approach works very well in reproducing the phase diagrams, fluctuations, and finite size effects of the microscopic model, while the \dCG approach proposed in our recent work [Phys. Rev. E 82, 011107(2010)] does not.