Topology Inference for Network Systems: Causality Perspective and Nonasymptotic Performance

Topology inference for network systems (NSs) plays a crucial role in many areas. This article advocates a causality-based method based on noisy observations from a single trajectory of an NS, which is represented by the state-space model with general directed topology. Specifically, we first prove its close relationships with the ideal Granger estimator for multiple trajectories and the traditional ordinary least squares (OLS) estimator for a single trajectory. Along with this line, we analyze the nonasymptotic inference performance of the proposed method by taking the OLS estimator as a reference, covering both asymptotically and marginally stable systems. The derived convergence rates and accuracy results suggest the proposed method has better performance in addressing potentially correlated observations and achieves zero inference error asymptotically. Besides, an online/recursive version of our method is established for efficient computation or time-varying cases. Extensions on NSs with nonlinear dynamics are also discussed. Comprehensive tests corroborate the theoretical findings and comparisons with other algorithms highlight the superiority of the proposed method.