AI 中文总结
该研究将时间网络传播动力学映射到时间事件图可达性，为易感-感染-易感模型等提供理论计算框架，通过事件图分量分析获时间网络流行率与阈值，计算优势显著。
AI 中文摘要
静态网络中的传播物理学已通过与渗流理论的映射得到充分理解。本文表明，时间网络上的传播动力学可类似地映射到时间事件图中的可达性，这为一类过程（如易感-感染-易感模型的变体）提供了理论和计算框架。无需显式模拟，通过事件图的分量分析，我们可获得任意度和事件间时间分布的时间网络的流行率并推导流行阈值，与显式模拟相比具有显著的计算优势。
英文摘要
The physics of spreading in static networks is well understood through mappings to percolation. We show that spreading dynamics on temporal networks can analogously be mapped to reachability in temporal event graphs. This provides a theoretical and computational framework for a class of processes, such as variants of the susceptible-infected-susceptible model. Without explicit simulations, through the component analysis of event graphs, we obtain epidemic prevalence and derive epidemic thresholds for temporal networks with arbitrary degree and inter-event time distributions, with significant computational advantages as compared to explicit simulations.
Comments8 pages, 5 figures
