由平台诱导的群组解散与个体重连共同作用的有害信息自适应高阶传播
Control of Harmful Information Spreading on Adaptive Higher-Order Networks via Group Dissolution
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中文总结 AI 辅助
本文构建结合平台群组解散与用户自适应重连的自适应高阶传播模型，揭示群组解散的有效窗口，发现高阶强化扩大该窗口、重连同质性缩小该窗口，为遏制有害信息的平台策略提供依据。
中文摘要 AI 辅助
遏制有害信息传播仍是一项关键挑战，这促使平台层面采取群组解散等干预措施以切断传播链。然而在实践中，受解散影响的用户常表现出自适应行为，会重连形成新群组。目前仍不清楚这两种机制如何共同影响信息传播，以及群组解散是否仍能有效抑制传播。本文构建了一种自适应高阶传播模型，将平台诱导的群组解散与用户自适应重连相结合，并推导了理论框架。值得注意的是，研究揭示了群组解散的有效窗口，其以临界感染率为界：高于该阈值时，解散会适得其反并放大信息流行度；在该窗口内，解散作用呈非单调性，初期会加剧流行度，随后在超过临界解散率后通过不连续转变消除传播。研究还表明，高阶强化会扩大群组解散仍有效的感染率窗口，而重连同质性会大幅缩小该窗口。在经验超图上的模拟也验证了这些发现。本研究强调了自上而下的平台干预与自下而上的用户自适应之间的相互作用，凸显了在设计遏制有害信息的策略时需考虑自适应响应，以避免意外放大。
英文摘要
Curbing harmful information contagion remains a critical challenge, motivating platform-level interventions such as group dissolution to sever transmission chains. However, in practice, users affected by dissolution often exhibit adaptive behavior, rewiring to form new groups. Yet, it remains unclear how these two mechanisms jointly shape information contagion and whether group dissolution remains effective in suppressing it. Here, we develop an adaptive higher-order contagion model that integrates platform-induced group dissolution with user adaptive rewiring, and derive a theoretical framework. Notably, we reveal an effective window for group dissolution, bounded by a critical infection rate. Above this threshold, dissolution backfires and amplifies information prevalence. Within this window, dissolution acts non-monotonically, initially exacerbating prevalence before eradicating contagion via a discontinuous transition beyond a critical dissolution rate. We further show that higher-order reinforcement expands this infection-rate window over which dissolution remains effective, whereas rewiring homophily substantially narrows it. Simulations on empirical hypergraph also validate these findings. Our work highlights the interplay between top-down platform interventions and bottom-up user adaptation, underscoring the need to account for adaptive responses when designing strategies to curb harmful information without unintended amplification.
发表机构
- Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
