Corruption Detection in Networks of Bi-directional Dynamical Systems

Venkat Ram Subramanian, Andrew Lamperski, Murti V. Salapaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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 languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728113982
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference58th IEEE Conference on Decision and Control, CDC 2019

Bibliographical note

Funding Information:
The authors are with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455,,, Work supported in part by NSF CMMI 1727096.

Publisher Copyright:
© 2019 IEEE.


Dive into the research topics of 'Corruption Detection in Networks of Bi-directional Dynamical Systems'. Together they form a unique fingerprint.

Cite this