Multivariate Gaussian Network Structure Learning

TL;DR

This paper introduces a new method for learning the structure of multivariate Gaussian networks using a convex optimization approach, demonstrating its effectiveness through simulations and real data applications.

Contribution

It proposes a novel group-penalized regression framework for Gaussian network structure learning with proven consistency and computational efficiency.

Findings

The estimator accurately recovers the true network structure as sample size grows.

Simulation results show the method outperforms existing procedures.

Applied to real datasets, it reveals meaningful biological and industrial connections.

Abstract

We consider a graphical model where a multivariate normal vector is associated with each node of the underlying graph and estimate the graphical structure. We minimize a loss function obtained by regressing the vector at each node on those at the remaining ones under a group penalty. We show that the proposed estimator can be computed by a fast convex optimization algorithm. We show that as the sample size increases, the estimated regression coefficients and the correct graphical structure are correctly estimated with probability tending to one. By extensive simulations, we show the superiority of the proposed method over comparable procedures. We apply the technique on two real datasets. The first one is to identify gene and protein networks showing up in cancer cell lines, and the second one is to reveal the connections among different industries in the US.