Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models

We focus on integrating different types of extra knowledge (other than the observed samples) for estimating the sparse structure change between two p-dimensional Gaussian Graphical Models (i.e. differential GGMs). Previous differential GGM estimators either fail to include additional knowledge or cannot scale up to a high-dimensional (large p) situation. This paper proposes a novel method KDiffNet that incorporates Additional Knowledge in identifying Differential Networks via an Elementary Estimator. We design a novel hybrid norm as a superposition of two structured norms guided by the extra edge information and the additional node group knowledge. KDiffNet is solved through a fast parallel proximal algorithm, enabling it to work in large-scale settings. KDiffNet can incorporate various combinations of existing knowledge without re-designing the optimization. Through rigorous statistical analysis we show that, while considering more evidence, KDiffNet achieves the same convergence rate as the state-of-the-art. Empirically on multiple synthetic datasets and one real-world fMRI brain data, KDiffNet significantly outperforms the cutting edge baselines with regard to the prediction performance, while achieving the same level of time cost or less.