Blockmodels: A R-package for estimating in Latent Block Model and   Stochastic Block Model, with various probability functions, with or without   covariates
  Analysis of the topology of a graph, regular or bipartite one, can be done by clustering for regular ones or co-clustering for bipartite ones. The Stochastic Block Model and the Latent Block Model are two models, which are very similar for respectively regular and bipartite graphs, based on probabilistic models. Initially developed for binary graphs, these models have been extended to valued networks with optional covariates on the edges. This paper present a implementation of a Variational EM algorithm for Stochastic Block Model and Latent Block Model for some common probability functions, Bernoulli, Gaussian and Poisson, without or with covariates, with some standard flavors, like multivariate extensions. This implementation allow automatic group number exploration and selection via the ICL criterion, and allow analyze networks with thousands of nodes in a reasonable amount of time. 
Jean-Benoist Leger
stat.CO