发表机构
Tongji University; State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学; 同济大学自主智能无人系统全国重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了一种分析降维框架，将高阶网络动力学映射到有效低维系统，揭示了高阶相互作用对相变的影响及系统鲁棒性与连接匹配的关系，为理解高阶网络临界转变提供通用理论。
AI 中文摘要
高阶网络展现出传统成对模型无法捕捉的丰富临界现象。本文中，我们开发了一种分析降维框架，该框架将高阶网络动力学映射到有效的低维系统，可准确预测 tipping 边界、双稳态区域及相变性质。我们在一系列动力学过程中验证了该框架的有效性，揭示了高阶相互作用对相变连续性和滞后效应的不同影响。此外，我们发现系统鲁棒性显著依赖于成对连接与高阶连接的匹配程度，同配混合会增强向活跃状态的 tipping。本研究结果为理解高阶网络中的临界转变建立了通用理论，为预测和管理复杂系统中的系统性风险提供了新见解。
英文摘要
Higher-order networks exhibit rich critical phenomena that cannot be captured by traditional pairwise models. Here, we develop an analytical dimension-reduction framework that maps higher-order networked dynamics onto an effective low-dimensional system, allowing accurate prediction of tipping boundaries, bistability regions, and the nature of phase transitions. We demonstrate the power of this framework across a range of dynamical processes, revealing distinct effects of higher-order interactions on transition continuity and hysteresis. Furthermore, we find that system resilience exhibits a profound dependence on the alignment between pairwise and higher-order connectivity, with assortative mixing enhancing tipping toward active states. Our findings establish a general theory for understanding the critical transitions in higher-order networks, offering new insights for anticipating and managing systemic risk in complex systems.
