  The mixed membership stochastic blockmodel (MMSB) is a popular Bayesian
network model for community detection. Fitting such large Bayesian network
models quickly becomes computationally infeasible when the number of nodes
grows into hundreds of thousands and millions. In this paper we propose a novel
mini-batch strategy based on aggregated relational data that leverages nodal
information to fit MMSB to massive networks. We describe a scalable inference
method that can utilize nodal information that often accompanies real-world
networks. Conditioning on this extra information leads to a model that admits a
parallel stochastic variational inference algorithm, utilizing stochastic
gradients of bipartite graph formed from aggregated network ties between node
subpopulations. We apply our method to a citation network with over two million
nodes and 25 million edges, capturing explainable structure in this network.
Our method recovers parameters and achieves better convergence on simulated
networks generated according to the MMSB.
