  Learning the differential statistical dependency network between two contexts
is essential for many real-life applications, mostly in the high dimensional
low sample regime. In this paper, we propose a novel differential network
estimator that allows integrating various sources of knowledge beyond data
samples. The proposed estimator is scalable to a large number of variables and
achieves a sharp asymptotic convergence rate. Empirical experiments on
extensive simulated data and four real-world applications (one on neuroimaging
and three from functional genomics) show that our approach achieves improved
differential network estimation and provides better supports to downstream
tasks like classification. Our results highlight significant benefits of
integrating group, spatial and anatomic knowledge during differential genetic
network identification and brain connectome change discovery.
