AI 中文总结
研究时间高阶网络的免疫问题，提出针对此类网络的免疫策略和理论框架，揭示流行率与免疫比例关系，提出HIC策略及自我中心策略，其性能优越且随流行率变化，推进网络免疫理解，助力传染控制。
AI 中文摘要
网络免疫是控制从传染病到错误信息传播等传染过程的有力工具。以往研究集中在成对或静态网络，时间高阶网络中的免疫动力学仍了解不足。本文介绍免疫策略并为此类时间系统开发理论框架。首先揭示流行率随免疫比例变化呈现双稳性和不连续转变，表明免疫效果取决于初始流行率，与成对网络有根本不同。基于此提出高感染贡献（HIC）策略，性能优于所有评估的启发式策略。还引入仅利用局部观测的自我中心策略，且最优自我中心策略随传染流行率变化。推进了对网络免疫的理解，为时间高阶网络中有效控制传染铺平道路。
英文摘要
Network immunization is a powerful tool for controlling contagion processes ranging from infectious diseases to misinformation diffusion. While prior works have focused on pairwise or static networks, immunization dynamics in temporal higher-order networks remain poorly understood. Here, we introduce immunization strategies and develop a theoretical framework tailored for such temporal systems. Firstly, we reveal bistability and discontinuous transitions in prevalence as the immunization fraction varies. This implies that immunization effectiveness depends on the initial prevalence, marking a fundamental departure from pairwise networks. Building on this prevalence-dependent behavior, we propose the High Infection Contribution (HIC) strategy, demonstrating its superior performance over all evaluated heuristic strategies. Furthermore, we introduce egocentric strategies by leveraging solely local observations. Notably, the optimal egocentric strategy shifts with the contagion prevalence. Our work advances the understanding of network immunization, paving the way for effective contagion control in temporal higher-order networks.
