  We consider a stochastic blockmodel equipped with node covariate information,
that is helpful in analyzing social network data. The key objective is to
obtain maximum likelihood estimates of the model parameters. For this task, we
devise a fast, scalable Monte Carlo EM type algorithm based on case-control
approximation of the log-likelihood coupled with a subsampling approach. A key
feature of the proposed algorithm is its parallelizability, by processing
portions of the data on several cores, while leveraging communication of key
statistics across the cores during each iteration of the algorithm. The
performance of the algorithm is evaluated on synthetic data sets and compared
with competing methods for blockmodel parameter estimation. We also illustrate
the model on data from a Facebook derived social network enhanced with node
covariate information.
