Network Structure Identification From Corrupt Data Streams

• 成果类型: Article
• 署名作者: Subramanian, Venkat Ram; Lamperski, Andrew; Salapaka, Murti, V
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3040952
• 发表日期: 2021
• 页码: 5314-5325
• 关键词: Linear systems; Directed graphs; Markov processes; data models; ETHICS; uncertainty; Transfer functions; Graphical Models; System identification; time series analysis

摘要

Complex networked systems can be modeled as graphs with nodes representing the agents and links describing the dynamic coupling between them. Previous work on network identification has shown that the network structure of linear time-invariant (LTI) systems can be reconstructed from the joint power spectrum of the data streams. These results assumed that data are perfectly measured. However, real-world data are subject to many corruptions, such as inaccurate time-stamps, noise, and data loss. We show that identifying the structure of linear time-invariant (LTI) systems using corrupt measurements results in the inference of erroneous links. We provide an exact characterization and prove that such erroneous links are restricted to the neighborhood of the perturbed node. We extend the analysis of LTI systems to the case of Markov random fields with corrupt measurements. We show that data corruption in Markov random fields results in spurious probabilistic relationships in precisely the locations, where spurious links arise in LTI systems.