Renormalizing Sznajd model on complex networks taking into account the effects of growth mechanisms

TL;DR

This paper develops a renormalization method to analyze the Sznajd opinion model on complex networks, capturing how growth mechanisms influence consensus formation and transition sharpness.

Contribution

It introduces a renormalization approach that accounts for growth effects, providing analytical expressions for consensus probability on complex networks.

Findings

Reproduces sharp transition on fixed networks

Models smooth transition on growing networks

Provides analytical insight into opinion dynamics

Abstract

We present a renormalization approach to solve the Sznajd opinion formation model on complex networks. For the case of two opinions, we present an expression of the probability of reaching consensus for a given opinion as a function of the initial fraction of agents with that opinion. The calculations reproduce the sharp transition of the model on a fixed network, as well as the recently observed smooth function for the model when simulated on a growing complex networks.