Transitivity reinforcement in the coevolving voter model

TL;DR

This paper introduces a modified coevolving voter model that explicitly reinforces network clustering, demonstrating how transitivity influences network dynamics through extensive simulations and semi-analytical predictions.

Contribution

It presents a novel coevolving voter model that maintains and reinforces clustering, contrasting with previous models that randomize such properties.

Findings

Reinforcing transitivity alters the phase transitions in the model.

Clustering affects the dynamical states and stability of the network.

Semi-analytical methods successfully predict the model's behavior.

Abstract

One of the fundamental structural properties of many networks is triangle closure. Whereas the influence of this transitivity on a variety of contagion dynamics has been previously explored, existing models of coevolving or adaptive network systems use rewiring rules that randomize away this important property. In contrast, we study here a modified coevolving voter model dynamics that explicitly reinforces and maintains such clustering. Employing extensive numerical simulations, we establish that the transitions and dynamical states observed in coevolving voter model networks without clustering are altered by reinforcing transitivity in the model. We then use a semi-analytical framework in terms of approximate master equations to predict the dynamical behaviors of the model for a variety of parameter settings.