GGM knockoff filter: False Discovery Rate Control for Gaussian Graphical Models

Understand

We propose a new method to learn the structure of a Gaussian graphical model with finite sample false discovery rate control.

• Our method builds on the knockoff framework of Barber and Cand\`{e}s for linear models.
• We extend their approach to the graphical model setting by using a local (node-based) and a global (graph-based) step: we construct knockoffs and feature statistics for each node locally, and then solve a global optimization problem to determine a threshold for each node.
• We then estimate the neighborhood of each node, by comparing its feature statistics to its threshold, resulting in our graph estimate.