Anomalous subgraph detection via Sparse Principal Component Analysis

Navraj Singh, Benjamin A. Miller, Nadya T. Bliss, Patrick J. Wolfe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Network datasets have become ubiquitous in many fields of study in recent years. In this paper we investigate a problem with applicability to a wide variety of domains detecting small, anomalous subgraphs in a background graph. We characterize the anomaly in a subgraph via the well-known notion of network modularity, and we show that the optimization problem formulation resulting from our setup is very similar to a recently introduced technique in statistics called Sparse Principal Component Analysis (Sparse PCA), which is an extension of the classical PCA algorithm. The exact version of our problem formulation is a hard combinatorial optimization problem, so we consider a recently introduced semidefinite programming relaxation of the Sparse PCA problem. We show via results on simulated data that the technique is very promising.

Original languageEnglish
Title of host publication2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Pages485-488
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
Duration: 28 Jun 201130 Jun 2011

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Country/TerritoryFrance
CityNice
Period28/06/1130/06/11

Keywords

  • Anomaly detection
  • community detection
  • graph analysis
  • semidefinite programming
  • sparse principal component analysis

ASJC Scopus subject areas

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

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