  We introduce a Bayesian estimator of the underlying class structure in the
stochastic block model, when the number of classes is known. The estimator is
the posterior mode corresponding to a Dirichlet prior on the class proportions,
a generalized Bernoulli prior on the class labels, and a beta prior on the edge
probabilities. We show that this estimator is strongly consistent when the
expected degree is at least of order $\log^2{n}$, where $n$ is the number of
nodes in the network.
