  Our concern is selecting the concentration matrix's nonzero coefficients for
a sparse Gaussian graphical model in a high-dimensional setting. This
corresponds to estimating the graph of conditional dependencies between the
variables. We describe a novel framework taking into account a latent structure
on the concentration matrix. This latent structure is used to drive a penalty
matrix and thus to recover a graphical model with a constrained topology. Our
method uses an $\ell_1$ penalized likelihood criterion. Inference of the graph
of conditional dependencies between the variates and of the hidden variables is
performed simultaneously in an iterative \textsc{em}-like algorithm. The
performances of our method is illustrated on synthetic as well as real data,
the latter concerning breast cancer.
