  We investigate in this paper the estimation of Gaussian graphs by model
selection from a non-asymptotic point of view. We start from a n-sample of a
Gaussian law P_C in R^p and focus on the disadvantageous case where n is
smaller than p. To estimate the graph of conditional dependences of P_C, we
introduce a collection of candidate graphs and then select one of them by
minimizing a penalized empirical risk. Our main result assess the performance
of the procedure in a non-asymptotic setting. We pay a special attention to the
maximal degree D of the graphs that we can handle, which turns to be roughly
n/(2 log p).
