Modeling complex networked systems as graphs is prevalent, with nodes representing the agents and the links describing a notion of dynamic coupling between them. Passive methods to identify such influence pathways from data are central to many applications. However, dynamically related data-streams originating at different sources are prone to corruption caused by asynchronous time-stamps of different streams, packet drops and noise. Earlier results have shown that spurious links are inferred in the graph structure identified using corrupt data-streams. In this article, we provide a novel approach to detect the location of corrupt agents in the network solely by observing the inferred directed graph. Here, the generative system that yields the data admits bidirectionally coupled nonlinear dynamic influences between agents. A simple, but novel and effective approach, using graph theory tools is presented to arrive at the results.
|Original language||English (US)|
|Title of host publication||2019 IEEE 58th Conference on Decision and Control, CDC 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Dec 2019|
|Event||58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France|
Duration: Dec 11 2019 → Dec 13 2019
|Name||Proceedings of the IEEE Conference on Decision and Control|
|Conference||58th IEEE Conference on Decision and Control, CDC 2019|
|Period||12/11/19 → 12/13/19|
Bibliographical noteFunding Information:
The authors are with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA.firstname.lastname@example.org, email@example.com, firstname.lastname@example.org Work supported in part by NSF CMMI 1727096.