Inferring Directed Graphs for Networks from Corrupt Data-Streams

Venkat Ram Subramanian, Andrew Lamperski, Murti V. Salapaka

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

5 Scopus citations


The structure of a complex networked system can be modeled as a graph with nodes representing the agents and the links describing a notion of dynamic coupling between them. Data-driven methods to identify such influence pathways is central to many application domains. However, such dynamically related data-streams originating at different sources are prone to corruption caused by asynchronous time-stamps, packet drops and noise. In this article, we provide a tight characterization of the connectivity structure of the agents that can be constructed based solely on measured data streams that are corrupted. A necessary and sufficient condition that delineates the effects of corruption on a set of nodes is obtained. Here, the generative system that yields the data admits nonlinear dynamic influences between agents and can involve feedback loops. Directed information based concepts are utilized in conjunction with tools from graphical models theory to arrive at the results.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538613955
StatePublished - Jul 2 2018
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018Dec 19 2018

Publication series

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


Conference57th IEEE Conference on Decision and Control, CDC 2018
Country/TerritoryUnited States

Bibliographical note

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

Funding Information:
Work supported in part by NSF CMMI 1727096.

Publisher Copyright:
© 2018 IEEE.


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