  Recent years have seen an increasing popularity of learning the sparse
\emph{changes} in Markov Networks. Changes in the structure of Markov Networks
reflect alternations of interactions between random variables under different
regimes and provide insights into the underlying system. While each individual
network structure can be complicated and difficult to learn, the overall change
from one network to another can be simple. This intuition gave birth to an
approach that \emph{directly} learns the sparse changes without modelling and
learning the individual (possibly dense) networks. In this paper, we review
such a direct learning method with some latest developments along this line of
research.
