Handle URI:
http://hdl.handle.net/10754/623599
Title:
Subgraph detection using graph signals
Authors:
Chepuri, Sundeep Prabhakar; Leus, Geert
Abstract:
In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are known, and derive an optimal Neyman-Pearson detector. Next, we derive a suboptimal detector for the case when the alternative graph is not known. The developed theory is illustrated with numerical experiments.
Citation:
Chepuri SP, Leus G (2016) Subgraph detection using graph signals. 2016 50th Asilomar Conference on Signals, Systems and Computers. Available: http://dx.doi.org/10.1109/acssc.2016.7869097.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 50th Asilomar Conference on Signals, Systems and Computers
KAUST Grant Number:
OSR-2015-Sensors-2700
Conference/Event name:
50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Issue Date:
6-Mar-2017
DOI:
10.1109/acssc.2016.7869097
Type:
Conference Paper
Sponsors:
This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorChepuri, Sundeep Prabhakaren
dc.contributor.authorLeus, Geerten
dc.date.accessioned2017-05-15T10:35:10Z-
dc.date.available2017-05-15T10:35:10Z-
dc.date.issued2017-03-06en
dc.identifier.citationChepuri SP, Leus G (2016) Subgraph detection using graph signals. 2016 50th Asilomar Conference on Signals, Systems and Computers. Available: http://dx.doi.org/10.1109/acssc.2016.7869097.en
dc.identifier.doi10.1109/acssc.2016.7869097en
dc.identifier.urihttp://hdl.handle.net/10754/623599-
dc.description.abstractIn this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are known, and derive an optimal Neyman-Pearson detector. Next, we derive a suboptimal detector for the case when the alternative graph is not known. The developed theory is illustrated with numerical experiments.en
dc.description.sponsorshipThis work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectlocally most powerful testen
dc.subjectGraph signal processingen
dc.subjectsubgraph detectionen
dc.subjecthypothesis testingen
dc.subjectquadratic detectoren
dc.titleSubgraph detection using graph signalsen
dc.typeConference Paperen
dc.identifier.journal2016 50th Asilomar Conference on Signals, Systems and Computersen
dc.conference.date2016-11-06 to 2016-11-09en
dc.conference.name50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016en
dc.conference.locationPacific Grove, CA, USAen
dc.contributor.institutionFaculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, The Netherlandsen
kaust.grant.numberOSR-2015-Sensors-2700en
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