Pair approximation for the \(q\)-voter model with independence on multiplex networks

• Published in: arXiv.org
• Main Authors: Gradowski, Tomasz, Krawiecki, Andrzej
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 14.08.2020
• Subjects: Approximation; Computer simulation; Critical point; Ferromagnetic phases; Ferromagnetism; Mathematical models; Multiplexing; Nodes; Parameters; Phase transitions; Topology; Transportation networks
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1908.00660

The \(q\)-voter model with independence is investigated on multiplex networks with fully overlapping layers in the form of various complex networks corresponding to different levels of social influence. Detailed studies are performed for the model on multiplex networks with two layers with identical degree distributions, obeying the LOCAL&AND and GLOBAL&AND spin update rules differing by the way in which the \(q\)-lobbies of neighbors within different layers exert their joint influence on the opinion of a given agent. Homogeneous pair approximation is derived for a general case of a two-state spin model on a multiplex network and its predictions are compared with results of Monte Carlo simulations of the above-mentioned \(q\)-voter model with independence for a broad range of parameters. As the parameter controlling the level of agents' independence is changed ferromagnetic phase transition occurs which can be first- or second-order, depending on the size of the lobby \(q\). Details of this transition, e.g., position of the critical points, depend on the topology and other features, e.g., the mean degree of nodes of the layers. If the mean degree of nodes in the layers is substantially larger than the size of the \(q\)-lobby good agreement is obtained between numerical results and theoretical predictions based on the homogeneous pair approximation concerning the order and details of the ferromagnetic transition. In the case of the model on multiplex networks with layers in the form of homogeneous Erdo\"s-Rényi and random regular graphs as well as weakly heterogeneous scale-free networks this agreement is quantitative, while in the case of layers in the form of strongly heterogeneous scale-free networks it is only qualitative. If the mean degree of nodes is small and comparable with \(q\) predictions of the homogeneous PA are in general even qualitatively wrong.