论文标题
动态遗憾分析，用于在线跟踪时变结构方程模型拓扑
Dynamic Regret Analysis for Online Tracking of Time-varying Structural Equation Model Topologies
论文作者
论文摘要
在复杂系统中识别变量之间的依赖性是网络科学中的重要问题。结构方程模型（SEM）已在许多领域中广泛用于拓扑推断，因为它们是可进行的，并且在模型中融合了外源性影响。基于静态SEM的拓扑识别在固定环境中很有用。但是，在许多应用中，人们都寻求了时间变化的潜在拓扑。本文提出了一种在线算法，以跟踪动态环境中稀疏时变拓扑，最重要的是，对性能保证进行了详细的分析。跟踪能力的特征是根据所提出算法的动态遗憾的结合。数值测试表明，所提出的算法可以跟踪不同时变拓扑模型下的变化。
Identifying dependencies among variables in a complex system is an important problem in network science. Structural equation models (SEM) have been used widely in many fields for topology inference, because they are tractable and incorporate exogenous influences in the model. Topology identification based on static SEM is useful in stationary environments; however, in many applications a time-varying underlying topology is sought. This paper presents an online algorithm to track sparse time-varying topologies in dynamic environments and most importantly, performs a detailed analysis on the performance guarantees. The tracking capability is characterized in terms of a bound on the dynamic regret of the proposed algorithm. Numerical tests show that the proposed algorithm can track changes under different models of time-varying topologies.
