具有高阶相互作用的多层网络的多尺度重建

中文摘要 AI 辅助

从动力学观测中推断网络结构是复杂系统中的一个基本逆问题。现有方法主要集中于单层或成对交互网络，使得具有高阶相互作用的多层系统的可重建性仍未被充分理解。在此，我们表明此类系统中的结构可辨识性由跨层传播耦合和网络异质性共同决定。基于耦合的认知-行为动力学，我们开发了一个多尺度推断框架，该框架能够解开相互作用的传播过程，并实现微观单纯形相互作用与宏观元种群组织的同步重建。在合成和实证网络上的数值实验展示了高重建性能，尤其是在由弱抑制和强促进耦合维持的非吸收动力学体系中。结果揭示了高阶多层网络中控制结构可辨识性的机制，并为多尺度复杂系统中的逆推断提供了一条通用途径。

英文摘要

Inferring network structure from dynamical observations is a fundamental inverse problem in complex systems. Existing approaches have largely focused on single-layer or pairwise interaction networks, leaving the reconstructability of multiplex systems with higher-order interactions poorly understood. Here, we show that structural identifiability in such systems is jointly governed by cross-layer spreading couplings and network heterogeneity. Building on coupled awareness-behavior dynamics, we develop a multiscale inference framework that disentangles interacting spreading processes and enables the simultaneous reconstruction of microscopic simplicial interactions and macroscopic metapopulation organization. Numerical experiments on synthetic and empirical networks demonstrate high reconstruction performance, particularly in non-absorbing dynamical regimes sustained by weak inhibitory and strong facilitatory couplings. Results reveal the mechanisms governing structural identifiability in higher-order multiplex networks and provide a general route for inverse inference in multiscale complex systems.

发表机构

• Hangzhou Normal University（杭州师范大学）
• National University of Defense Technology（国防科技大学）
• Beihang University（北京航空航天大学）
• University of Fribourg（弗里堡大学）

机构由 AI 辅助整理，请以论文原文为准。