Blume-Emery-Griffiths dynamics in social networks

TL;DR

This paper applies the Blume-Emery-Griffiths model to social networks, analyzing three-state opinion dynamics, phase transitions, and the effects of external perturbations and noise on opinion distributions.

Contribution

It introduces the BEG model into social network analysis, exploring phase transitions and the influence of external fields and noise on opinion formation.

Findings

Opinion time series follow Gaussian-like distributions.

External periodic perturbations induce phase transitions.

Thermo-noise and external fields have opposite effects on opinion distributions.

Abstract

We introduce the Blume-Emery-Griffiths (BEG) model in a social networks to describe the three-state dynamics of opinion formation. It shows that the probability distribution function of the time series of opinion is a Gaussian-like distribution. We also study the response of BEG model to the external periodic perturbation. One can observe that both the interior thermo-noise and the external field result in phase transition, which is a split phenomena of the opinion distributions. It is opposite between the effect acted on the opinion systems of the amplitude of the external field and of the thermo-noise.