Empowering Differential Networks Using Bayesian Analysis

TL;DR

This paper introduces a Bayesian method for estimating differential networks that improves accuracy and efficiency, demonstrated through synthetic data and COVID-19 analysis.

Contribution

It presents a novel Bayesian adaptive graphical lasso approach for differential network estimation with efficient threshold selection.

Findings

Bayesian DN outperforms existing methods in accuracy and structure recovery.

The method effectively detects changes in COVID-19 data across pandemic phases.

Synthetic experiments validate the approach's numerical and structural advantages.

Abstract

Differential networks (DN) are important tools for modeling the changes in conditional dependencies between multiple samples. A Bayesian approach for estimating DNs, from the classical viewpoint, is introduced with a computationally efficient threshold selection for graphical model determination. The algorithm separately estimates the precision matrices of the DN using the Bayesian adaptive graphical lasso procedure. Synthetic experiments illustrate that the Bayesian DN performs exceptionally well in numerical accuracy and graphical structure determination in comparison to state-of-the-art methods. The proposed method is applied to South African COVID-$19$ data to investigate the change in DN structure between various phases of the pandemic.