Toward matched filter optimization for subgraph detection in dynamic networks

Benjamin A. Miller, Nadya T. Bliss

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

6 Scopus citations

Abstract

This paper outlines techniques for optimization of filter coefficients in a spectral framework for anomalous subgraph detection. Restricting the scope to the detection of a known signal in i.i.d. noise, the optimal coefficients for maximizing the signal's power are shown to be found via a rank-1 tensor approximation of the subgraph's dynamic topology. While this technique optimizes our power metric, a filter based on average degree is shown in simulation to work nearly as well in terms of power maximization and detection performance, and better separates the signal from the noise in the eigenspace.

Original languageEnglish (US)
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages113-116
Number of pages4
DOIs
StatePublished - Nov 6 2012
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: Aug 5 2012Aug 8 2012

Publication series

Name2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Other

Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
CountryUnited States
CityAnn Arbor, MI
Period8/5/128/8/12

Keywords

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

ASJC Scopus subject areas

  • Signal Processing

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  • Cite this

    Miller, B. A., & Bliss, N. T. (2012). Toward matched filter optimization for subgraph detection in dynamic networks. In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 (pp. 113-116). [6319635] (2012 IEEE Statistical Signal Processing Workshop, SSP 2012). https://doi.org/10.1109/SSP.2012.6319635