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

24 Scopus citations


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
Number of pages4
StatePublished - 2011
Externally publishedYes
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


Other2011 IEEE Statistical Signal Processing Workshop, SSP 2011


  • 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


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