# An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks

@article{Kan2021AnAB, title={An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks}, author={Unchitta Kan and Michelle Feng and M. A. Porter}, journal={ArXiv}, year={2021}, volume={abs/2112.05856} }

Individuals who interact with each other in social networks often exchange ideas and influence each other's opinions. A popular approach to studying the dynamics of opinion spread on networks is by examining bounded-confidence (BC) models, in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other opinions if they lie within some confidence bound of their own opinion. We extend the Deffuant--Weisbuch (DW) model, which is a well-known BC…

## 2 Citations

A Bounded-Confidence Model of Opinion Dynamics with Heterogeneous Node-Activity Levels

- Computer ScienceArXiv
- 2022

This work generalizes the Deffuant–Weisbuch bounded-confidence model (BCM) of opinion dynamics by incorporating node weights, and demonstrates that introducing heterogeneous node weights results in longer convergence times and more opinion fragmentation than in a baseline DW model.

A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs

- MathematicsSIAM J. Appl. Dyn. Syst.
- 2022

This paper extends an asynchronous boundedconfidence model (BCM) on graphs, in which nodes are connected pairwise by edges, to hypergraphs and shows that the hypergraph BCM converges to consensus under a wide range of initial conditions for the opinions of the nodes.

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