  Differential network is an important tool to capture the changes of
conditional correlations under two sample cases. In this paper, we introduce a
fast iterative algorithm to recover the differential network for
high-dimensional data. The computation complexity of our algorithm is linear in
the sample size and the number of parameters, which is optimal in the sense
that it is of the same order as computing two sample covariance matrices. The
proposed method is appealing for high-dimensional data with a small sample
size. The experiments on simulated and real data sets show that the proposed
algorithm outperforms other existing methods.
