Anomalous subgraph detection via Sparse Principal Component Analysis

Navraj Singh, Benjamin A. Miller, Nadya T. Bliss, Patrick J. Wolfe
2011 2011 IEEE Statistical Signal Processing Workshop (SSP)  
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
more » ... alysis (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.
doi:10.1109/ssp.2011.5967738 fatcat:lkmrxmp2zzewdpahgjk3jzrjzm