# Effects of Stubbornness on Opinion Dynamics

arXiv: https://arxiv.org/abs/2208.04160 · HTML: https://mlsanity.com/paper/2208.04160/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2022.

## Built on by (most cited first, 3 of 3)

- [Rebalancing Social Feed to Minimize Polarization and Disagreement](https://mlsanity.com/paper/2308.14486/) (2023)
- [Graph Neural Diffusion via Generalized Opinion Dynamics](https://mlsanity.com/paper/2508.11249/) (2025)
- [H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance](https://mlsanity.com/paper/2507.13370/) (2025)

## Abstract (arXiv)

As an important factor governing opinion dynamics, stubbornness strongly affects various aspects of opinion formation. However, a systematically theoretical study about the influences of heterogeneous stubbornness on opinion dynamics is still lacking. In this paper, we study a popular opinion model in the presence of inhomogeneous stubbornness. We show analytically that heterogeneous stubbornness has a great impact on convergence time, expressed opinion of every node, and the overall expressed opinion. We provide an explanation of the expressed opinion in terms of stubbornness-dependent spanning diverging forests. We propose quantitative indicators to quantify some social concepts, including conflict, disagreement, and polarization by incorporating heterogeneous stubbornness, and develop a nearly linear time algorithm to approximate these quantities, which has a proved theoretical guarantee for the error of each quantity. To demonstrate the performance of our algorithm, we perform extensive experiments on a large set of real networks, which indicate that our algorithm is both efficient and effective, scalable to large networks with millions of nodes.