H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909885280550912 |
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| author | Guo, Shijun Xu, Haoran Yang, Yaming Guan, Ziyu Zhao, Wei Zhang, Xinyi Song, Yishan |
| author_facet | Guo, Shijun Xu, Haoran Yang, Yaming Guan, Ziyu Zhao, Wei Zhang, Xinyi Song, Yishan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_13370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance Guo, Shijun Xu, Haoran Yang, Yaming Guan, Ziyu Zhao, Wei Zhang, Xinyi Song, Yishan Social and Information Networks Artificial Intelligence Multiagent Systems 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. |
| title | H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance |
| topic | Social and Information Networks Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2507.13370 |