2024/12/23 by Anna Plaksienko, Magne Thoresen, Plaksienko, Anna +3
Engineering · #Applications (stat.AP) #Control Systems and Identification #FOS: Computer and information sciences #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2412.17922
openalex publication_date 2024/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We provide a review and a comparison of methods for differential network estimation in Gaussian graphical models with focus on structure learning. We consider the case of two datasets from distributions associated with two graphical models. In our simulations, we use five different methods to estimate differential networks. We vary graph structure and sparsity to explore their influence on performance in terms of power and false discovery rate. We demonstrate empirically that presence of hubs proves to be a challenge for all the methods, as well as increased density. We suggest local and global properties that are associated with this challenge. Direct estimation with lasso penalized D-trace loss is shown to perform the best across all combinations of network structure and sparsity.