Signal detection on graphs: Bernoulli noise model

Siheng Chen, Yaoqing Yang, Aarti Singh, Jelena Kovačević

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

Abstract

We consider detecting localized binary attributes on a graph. A localized binary attribute means that the nodes activated by the attribute form form a subgraph that can be easily separated from the other nodes. We formulate a statistical hypothesis test to decide whether a given attribute is localized or not. We propose two statistics: graph wavelet statistic and graph scan statistic. Both are shown to be efficient and statistically effective. We further apply the proposed methods to rank research keywords as attributes in a coauthorship network collected from IEEE Xplore. The experimental results show that the proposed graph wavelet statistic and graph scan statistic are effective and efficient.1

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages395-399
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

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ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Chen, S., Yang, Y., Singh, A., & Kovačević, J. (2017). Signal detection on graphs: Bernoulli noise model. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 395-399). [7905871] (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7905871