Subgraph Detection using Graph Signals

Sundeep Prabhakar Chepuri, Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 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 languageEnglish
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages532-534
Number of pages3
ISBN (Electronic)978-1-5386-3954-2
DOIs
Publication statusPublished - 1 Nov 2016
Event50th Asilomar ConFerence on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: 6 May 20169 May 2016
http://www.asilomarsscconf.org/

Conference

Conference50th Asilomar ConFerence on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period6/05/169/05/16
Internet address

Keywords

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

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