统一分析复杂网络同步稳定性,让大师稳定函数更易用。
Master Stability Functions in Complex Networks
- 提出统一框架,适用于有向、多层及高阶耦合网络。
- 通过数值实验验证罗尔斯系统在扩散耦合下的同步稳定性。
- 适合研究网络同步的学者,尤其关注复杂系统稳定性者。
同步是自然与工程系统中一种基本且涌现的现象。理解同步状态的稳定性对保障各类复杂系统的功能至关重要。长期以来,大师稳定函数(MSF)被广泛用于分析同步稳定性,为耦合系统中的同步行为提供了深刻洞见。尽管该方法已应用25年,但针对不同网络系统中MSF的系统性研究仍缺失。本文提出一种简化且统一的MSF分析方法,涵盖无向与有向网络,扩展至多层网络并考虑层内与层间相互作用。同时,重新审视框架以融合高阶相互作用。通过数值分析耦合罗尔斯系统的同步行为,并提出确定MSF、识别稳定区域和分类函数的算法。本综述旨在以清晰结构呈现MSF在耦合动力网络中的系统研究,提升该工具可及性;并指出当前尚有未充分探索的同步分析场景。此外,还讨论了基于时间序列数据和机器学习的最新研究进展。
原文摘要 · Abstract (English)
Synchronization is an emergent and fundamental phenomenon in nature and engineered systems. Understanding the stability of a synchronized phenomenon is crucial for ensuring functionality in various complex systems. The stability of the synchronization phenomenon is extensively studied using the Master Stability Function (MSF). This powerful and elegant tool plays a pivotal role in determining the stability of synchronization states, providing deep insights into synchronization in coupled systems. Although MSF analysis has been used for 25 years to study the stability of synchronization states, a systematic investigation of MSF across various networked systems remains missing from the literature. In this article, we present a simplified and unified MSF analysis for diverse undirected and directed networked systems. We begin with the analytical MSF framework for pairwise-coupled identical systems with diffusive and natural coupling schemes and extend our analysis to directed networks and multilayer networks, considering both intra-layer and inter-layer interactions. Furthermore, we revisit the MSF framework to incorporate higher-order interactions alongside pairwise interactions. To enhance understanding, we also provide a numerical analysis of synchronization in coupled Rössler systems under pairwise diffusive coupling and propose algorithms for determining the MSF, identifying stability regimes, and classifying MSF functions. Overall, the primary goal of this review is to present a systematic study of MSF in coupled dynamical networks in a clear and structured manner, making this powerful tool more accessible. Furthermore, we highlight cases where the study of synchronization states using MSF remains underexplored. Additionally, we discuss recent research focusing on MSF analysis using time series data and machine learning approaches.
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