  Social media data are often modeled as heterogeneous graphs with multiple
types of nodes and edges. We present a discovery algorithm that first chooses a
"background" graph based on a user's analytical interest and then automatically
discovers subgraphs that are structurally and content-wise distinctly different
from the background graph. The technique combines the notion of a
\texttt{group-by} operation on a graph and the notion of subjective
interestingness, resulting in an automated discovery of interesting subgraphs.
Our experiments on a socio-political database show the effectiveness of our
technique.
