A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs

TL;DR

This paper extends a bounded-confidence opinion dynamics model from simple graphs to hypergraphs, demonstrating convergence, formation of echo chambers, opinion jumping, and phase transitions in convergence time.

Contribution

It introduces a hypergraph-based BCM, analyzes its convergence behavior, and reveals phenomena like opinion jumping and phase transitions not seen in dyadic models.

Findings

Hypergraph BCM converges to consensus under various initial conditions.

Echo chambers can form on hypergraphs with community structure.

Opinion jumping can occur, where opinions shift abruptly between clusters.

Abstract

People's opinions evolve over time as they interact with their friends, family, colleagues, and others. In the study of opinion dynamics on networks, one often encodes interactions between people in the form of dyadic relationships, but many social interactions in real life are polyadic (i.e., they involve three or more people). In this paper, we extend an asynchronous bounded-confidence model (BCM) on graphs, in which nodes are connected pairwise by edges, to an asynchronous BCM on hypergraphs, in which arbitrarily many nodes can be connected by a single hyperedge. We show that our hypergraph BCM converges to consensus under a wide range of initial conditions for the opinions of the nodes, including for non-uniform and asymmetric initial opinion distributions. We also show that, under suitable conditions, echo chambers can form on hypergraphs with community structure. We demonstrate that the opinions of individuals can sometimes jump from one opinion cluster to another in a single time step, a phenomenon (which we call ``opinion jumping'') that is not possible in standard dyadic BCMs. Additionally, we observe that there is a phase transition in the convergence time on {a complete hypergraph} when the variance $\sigma^2$ of the initial opinion distribution equals the confidence bound $c$. We prove that the convergence time grows at least exponentially fast with the number of nodes when $\sigma^2 > c$ and the initial opinions are normally distributed. Therefore, to determine the convergence properties of our hypergraph BCM when the variance and the number of hyperedges are both large, it is necessary to use analytical methods instead of relying only on Monte Carlo simulations.