Effects of Data Corruption on Network Identification using Directed Information
Effects of Data Corruption on Network Identification using Directed Information
复制标题
数据损坏对使用定向信息进行网络识别的影响
DOI：
10.1109/tac.2021.3093301
复制
发表时间：
2021
影响因子：
6.8
通讯作者：
Salapaka, Murti V.
中科院分区：
文献类型：
--
作者：
Subramanian, Venkat Ram;Lamperski, Andrew;Salapaka, Murti V.
复杂的网络系统可以建模并表示为图形，节点表示代理，链接描述它们之间的动态耦合。动态系统网络辨识的基本目标是辨识因果影响路径。然而，源自不同来源的动态相关数据流易于由异步时间戳、数据包丢弃和噪声引起的损坏。在这篇文章中，我们表明，识别因果结构使用腐败的测量结果的推断虚假的链接。一个必要和充分条件，描绘腐败的影响，一组节点。我们的理论适用于非线性系统和具有反馈回路的系统。我们的结果是通过对动态贝叶斯网络中条件有向信息的分析得到的。我们提供了一致性结果的条件DI估计，我们使用的几乎肯定收敛。
Complex networked systems can be modeled and represented as graphs, with nodes representing the agents and the links describing the dynamic coupling between them. The fundamental objective of network identification for dynamic systems is to identify causal influence pathways. However, dynamically related data streams that originate from different sources are prone to corruption caused by asynchronous time-stamps, packet drops, and noise. In this article, we show that identifying causal structure using corrupt measurements results in the inference of spurious links. A necessary and sufficient condition that delineates the effects of corruption on a set of nodes is obtained. Our theory applies to nonlinear systems, and systems with feedback loops. Our results are obtained by the analysis of conditional directed information (DI) in dynamic Bayesian networks. We provide consistency results for the conditional DI estimator that we use by showing almost-sure convergence.