不强制干预用户，通过分层模型加速共识形成。
H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance
- 构建专家与普通用户双层动态模型，区分角色行为特征。
- 共识达成速度提升22.0%至30.7%，无专家时仍能全局收敛。
- 非侵入式邻居过滤+长周期奖励机制，适合社会网络治理。
社交媒体的开放性促进了意见自由交流，但也带来了引导共识的挑战。现有方法常直接修改用户观点或强制跨群体连接，此类侵入式干预损害用户自主性，引发心理抵触并降低共识效率。且缺乏长期视角，局部共识常加剧宏观分裂。为此，本文提出层级化、非侵入式意见引导框架H-NeiFi：首先基于社会角色建立双层动态模型，考虑专家与非专家的行为特征；引入非侵入式邻居过滤机制，自适应调控用户通信路径；利用多智能体强化学习（MARL）优化信息传播路径，通过长期奖励函数避免直接干预用户交互。实验表明，H-NeiFi使共识速度提升22.0%至30.7%，且在无专家情况下仍可实现全局收敛。该方法在保护用户交互自主性的前提下，实现了自然高效的共识引导，为社交网络治理提供新范式。
原文摘要 · Abstract (English)
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.
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