Inferring the structure and scale of modular networks

We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network, based on variational Bayesian inference for stochastic block models. We show how our method extends previous work and addresses the “resolution limit problem”. We apply the technique to synthetic and real networks.