# Quickest Hub Discovery in Correlation Graphs

## TL;DR

This paper introduces a sequential testing method for quickly detecting and isolating hubs in high-dimensional correlation graphs, using novel summary statistics to improve accuracy and speed.

## Contribution

It proposes a new sequential test leveraging local and global statistics for efficient hub detection in high-dimensional Gaussian correlation graphs.

## Key findings

- The test is consistent in identifying hubs as false alarm rate approaches zero.
- Delay and false alarm performance are analytically characterized.
- Numerical results demonstrate the method's effectiveness.

## Abstract

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.