PT  - JOURNAL ARTICLE
AU  - Sekhon, Arshdeep
AU  - Wang, Beilun
AU  - Qi, Yanjun
TI  - Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models
AID  - 10.1101/716852
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 716852
4099  - http://biorxiv.org/content/early/2019/07/28/716852.short
4100  - http://biorxiv.org/content/early/2019/07/28/716852.full
AB  - 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.