Coordination Games on Multiplex Networks: Consensus, Convergence, and Stability of Opinion Dynamics

TL;DR

It is shown that multilayer interactions can induce or accelerate global consensus even when no single layer achieves it alone, and conversely, that individually coordinated layers may lose consensus once interconnected.

Abstract

This paper studies opinion dynamics in multilayer social networks. Extending a single-layer model, we formulate opinion updates as a synchronous coordination game in which agents minimize a local cost to stay close to their neighbors' opinions. We propose two coupling mechanisms: (i) a merged model that aggregates layers through weighted influences, and (ii) a switching model that periodically alternates across layers. Using random-walk and spectral analysis, we derive sufficient conditions for consensus, characterize convergence rates, and analyze stability under network perturbations. We show that multilayer interactions can induce or accelerate global consensus even when no single layer achieves it alone, and conversely, that individually coordinated layers may lose consensus once interconnected. Numerical experiments validate the theory and highlight the impact of layer weights and switching periods. These results clarify how cross-network interactions shape coordination and information diffusion across interconnected systems.

Figures (4)

• Figure 1: Merged-layer consensus experiments on random networks.
• Figure 2: Switching layer consensus experiments on random networks.
• Figure 3: Merged layers opinion dynamics on the high-school contact network mastrandrea2015contact for different weighting factors $\alpha$.
• Figure 4: Switching layers opinion dynamics on the high-school contact network mastrandrea2015contact with different switching period $k+1$.

Theorems & Definitions (27)

• Lemma II.1: ghaderi2014opinion, Lemma 2
• Corollary II.1
• Theorem II.1: schweitzer1968perturbation, Theorem 2
• Corollary II.2
• Proposition III.1
• proof
• Proposition III.2
• proof
• Theorem III.1: Rayleigh Theorem, horn2012matrix, Theorem 4.2.2
• Theorem III.2: Courant–Fischer Theorem, horn2012matrix, Theorem 4.2.6