@inproceedings{993074e0da5e44feaaa9138976eda373,
title = "Anomalous subgraph detection via Sparse Principal Component Analysis",
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.",
keywords = "Anomaly detection, community detection, graph analysis, semidefinite programming, sparse principal component analysis",
author = "Navraj Singh and Miller, {Benjamin A.} and Bliss, {Nadya T.} and Wolfe, {Patrick J.}",
year = "2011",
doi = "10.1109/SSP.2011.5967738",
language = "English (US)",
isbn = "9781457705700",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
pages = "485--488",
booktitle = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011",
note = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011 ; Conference date: 28-06-2011 Through 30-06-2011",
}