2017/02/04 by Taposh Banerjee, Banerjee, Taposh, Hero, Alfred O.
Computer Science · Physics and Astronomy · #Bayesian Modeling and Causal Inference #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1702.01225
openalex publication_date 2017/02/04 · openalex created_date 2017/09/15 · openalex updated_date 2026/07/28
A sequential test is proposed for detection and isolation of hubs in a correlation graph. Hubs in a correlation graph of a random vector are variables (nodes) that have a strong correlation edge. It is assumed that the random vectors are high-dimensional and are multivariate Gaussian distributed. The test employs a family of novel local and global summary statistics generated from small samples of the random vectors. Delay and false alarm analysis of the test is obtained and numerical results are provided to show that the test is consistent in identifying hubs, as the false alarm rate goes to zero.