# H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance

arXiv: https://arxiv.org/abs/2507.13370 · HTML: https://mlsanity.com/paper/2507.13370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Minimizing Polarization and Disagreement in Social Networks](https://mlsanity.com/paper/1712.09948/) (2017)
- [Learning Opinion Dynamics From Social Traces](https://mlsanity.com/paper/2006.01673/) (2020)
- [Controllability Metrics, Limitations and Algorithms for Complex Networks](https://mlsanity.com/paper/1308.1201/) (2013)
- [Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes](https://mlsanity.com/paper/2105.11152/) (2021)
- [Predicting Opinion Dynamics via Sociologically-Informed Neural Networks](https://mlsanity.com/paper/2207.03990/) (2022)
- [Opinion Dynamics with Backfire Effect and Biased Assimilation](https://mlsanity.com/paper/1903.11535/) (2019)
- [Effects of Stubbornness on Opinion Dynamics](https://mlsanity.com/paper/2208.04160/) (2022)
- [Optimal Opinion Control: The Campaign Problem](https://mlsanity.com/paper/1410.8419/) (2014)
- [Continuous-time Opinion Dynamics on Multiple Interdependent Topics](https://mlsanity.com/paper/1805.02836/) (2018)
- [Opinion shaping in social networks using reinforcement learning](https://mlsanity.com/paper/1910.08802/) (2019)
- [Steering opinion dynamics through control of social networks](https://mlsanity.com/paper/2404.09849/) (2024)
- [Optimal control of the convergence time in the Hegselmann--Krause dynamics](https://mlsanity.com/paper/1411.4814/) (2014)
- [Opinion dynamics under common influencer assumption or leadership control](https://mlsanity.com/paper/2407.16901/) (2024)

## Abstract (arXiv)

The openness of social media enables the free exchange of opinions, but it also presents challenges in guiding opinion evolution towards global consensus. Existing methods often directly modify user views or enforce cross-group connections. These intrusive interventions undermine user autonomy, provoke psychological resistance, and reduce the efficiency of global consensus. Additionally, due to the lack of a long-term perspective, promoting local consensus often exacerbates divisions at the macro level. To address these issues, we propose the hierarchical, non-intrusive opinion guidance framework, H-NeiFi. It first establishes a two-layer dynamic model based on social roles, considering the behavioral characteristics of both experts and non-experts. Additionally, we introduce a non-intrusive neighbor filtering method that adaptively controls user communication channels. Using multi-agent reinforcement learning (MARL), we optimize information propagation paths through a long-term reward function, avoiding direct interference with user interactions. Experiments show that H-NeiFi increases consensus speed by 22.0% to 30.7% and maintains global convergence even in the absence of experts. This approach enables natural and efficient consensus guidance by protecting user interaction autonomy, offering a new paradigm for social network governance.