Much recent work has focused on community detection, or the task of
identifying sets of similar nodes from network topology. Underlying
this work is the implicit assumption that inferred communities inform
node attributes or function in a meaningful and useful sense. We
investigate these ideas by phrasing community detection as Bayesian
inference, which gives rise to a scalable and efficient variational
algorithm for fitting and comparing network models.  We show how
several existing methods for community detection can be described as
variant, special, or limiting cases of our work, and how the method
overcomes the "resolution limit" problem, accurately recovering both
the structure and scale of modular networks.  We apply the resulting
algorithm to several real networks and study the relationship between
identified topological communities and known node attributes.