Matched filtering for subgraph detection in dynamic networks

Benjamin A. Miller, Michelle S. Beard, Nadya T. Bliss

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

19 Scopus citations

Abstract

Graphs are high-dimensional, non-Euclidean data, whose utility spans a wide variety of disciplines. While their non-Euclidean nature complicates the application of traditional signal processing paradigms, it is desirable to seek an analogous detection framework. In this paper we present a matched filtering method for graph sequences, extending to a dynamic setting a previous method for the detection of anomalously dense subgraphs in a large background. In simulation, we show that this temporal integration technique enables the detection of weak subgraph anomalies than are not detectable in the static case. We also demonstrate background/foreground separation using a real background graph based on a computer network.

Original languageEnglish (US)
Title of host publication2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Pages509-512
Number of pages4
DOIs
StatePublished - Sep 5 2011
Event2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
Duration: Jun 28 2011Jun 30 2011

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2011 IEEE Statistical Signal Processing Workshop, SSP 2011
CountryFrance
CityNice
Period6/28/116/30/11

Keywords

  • community detection
  • dynamic graphs
  • graph algorithms
  • matched filtering
  • signal detection theory

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Matched filtering for subgraph detection in dynamic networks'. Together they form a unique fingerprint.

  • Cite this

    Miller, B. A., Beard, M. S., & Bliss, N. T. (2011). Matched filtering for subgraph detection in dynamic networks. In 2011 IEEE Statistical Signal Processing Workshop, SSP 2011 (pp. 509-512). [5967745] (IEEE Workshop on Statistical Signal Processing Proceedings). https://doi.org/10.1109/SSP.2011.5967745