Consensus Dynamics and Opinion Formation on Hypergraphs
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Abstract (arXiv)
In this chapter, we derive and analyse models for consensus dynamics on hypergraphs. As we discuss, unless there are nonlinear node interaction functions, it is always possible to rewrite the system in terms of a new network of effective pairwise node interactions, regardless of the initially underlying multi-way interaction structure. We thus focus on dynamics based on a certain class of non-linear interaction functions, which can model different sociological phenomena such as peer pressure and stubbornness. Unlike for linear consensus dynamics on networks, we show how our nonlinear model dynamics can cause shifts away from the average system state. We examine how these shifts are influenced by the distribution of the initial states, the underlying hypergraph structure and different forms of non-linear scaling of the node interaction function.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 May 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.
- 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 47
- 2023Learning the effective order of a hypergraph dynamical systemcited by 3
- 2025Higher-Order Regularization Learning on Hypergraphscited by 1
- 2025Higher-Order Regularization Learning on Hypergraphs30 Oct 2025
- 2023Learning the effective order of a hypergraph dynamical system2 Jun 2023
- 2022Equivariant Hypergraph Diffusion Neural Operators14 Jul 2022