  We consider the task of simultaneous clustering of the two node sets involved
in a bipartite network. The approach we adopt is based on use of the exact
integrated complete likelihood for the latent block model. Using this allows
one to infer the number of clusters as well as cluster memberships using a
greedy search. This gives a model-based clustering of the node sets.
Experiments on simulated bipartite network data show that the greedy search
approach is vastly more scalable than competing Markov chain Monte Carlo based
methods. Application to a number of real observed bipartite networks
demonstrate the algorithms discussed.
