  Many problems of interest in computer science and information theory can be
phrased in terms of a probability distribution over discrete variables
associated to the vertices of a large (but finite) sparse graph. In recent
years, considerable progress has been achieved by viewing these distributions
as Gibbs measures and applying to their study heuristic tools from statistical
physics. We review this approach and provide some results towards a rigorous
treatment of these problems.
