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

Inferring sparse Gaussian graphical models with latent structure