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

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

8 Citations (Scopus)

Abstract

In this paper we focus on subsampling stationary random processes that reside on 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
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers
Editors	Michael B. Matthews
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	532-534
Number of pages	3
ISBN (Electronic)	978-1-5386-3954-2
DOIs	https://doi.org/10.1109/ACSSC.2016.7869097
Publication status	Published - 1 Nov 2016
Event	50th Asilomar ConFerence on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: 6 May 2016 → 9 May 2016 http://www.asilomarsscconf.org/

Conference

Conference	50th Asilomar ConFerence on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove, CA
Period	6/05/16 → 9/05/16
Internet address	http://www.asilomarsscconf.org/

Keywords

locally most powerful test
Graph signal processing
subgraph detection
hypothesis testing
quadratic detector

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10.1109/ACSSC.2016.7869097

Cite this

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title = "Subgraph Detection using Graph Signals",

abstract = "In this paper we focus on subsampling stationary random processes that reside on 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.",

keywords = "locally most powerful test, Graph signal processing, subgraph detection, hypothesis testing, quadratic detector",

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

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publisher = "IEEE",

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AB - In this paper we focus on subsampling stationary random processes that reside on 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.

KW - locally most powerful test

KW - Graph signal processing

KW - subgraph detection

KW - hypothesis testing

KW - quadratic detector

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M3 - Conference contribution

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BT - Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers

A2 - Matthews, Michael B.

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T2 - 50th Asilomar ConFerence on Signals, Systems and Computers

Y2 - 6 May 2016 through 9 May 2016

ER -

Subgraph Detection using Graph Signals

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