Subgraph detection using graph signals

Sundeep Prabhakar Chepuri; Geert Leus

doi:10.1109/acssc.2016.7869097

Subgraph detection using graph signals

Sundeep Prabhakar Chepuri, Geert Leus

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

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.

Original language	English (US)
Title of host publication	2016 50th Asilomar Conference on Signals, Systems and Computers
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	532-535
Number of pages	4
ISBN (Print)	9781538639542
DOIs	https://doi.org/10.1109/acssc.2016.7869097
State	Published - Mar 6 2017
Externally published	Yes

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10.1109/acssc.2016.7869097

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title = "Subgraph detection using graph signals",

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.",

author = "Chepuri, {Sundeep Prabhakar} and Geert Leus",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700 Acknowledgements: This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2017",

month = mar,

day = "6",

doi = "10.1109/acssc.2016.7869097",

language = "English (US)",

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publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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T1 - Subgraph detection using graph signals

AU - Chepuri, Sundeep Prabhakar

AU - Leus, Geert

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700 Acknowledgements: This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2017/3/6

Y1 - 2017/3/6

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/623599

UR - http://ieeexplore.ieee.org/document/7869097/

UR - http://www.scopus.com/inward/record.url?scp=85016304870&partnerID=8YFLogxK

U2 - 10.1109/acssc.2016.7869097

DO - 10.1109/acssc.2016.7869097

M3 - Conference contribution

SN - 9781538639542

SP - 532

EP - 535

BT - 2016 50th Asilomar Conference on Signals, Systems and Computers

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Subgraph detection using graph signals

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