  Stochastic blockmodels allow us to represent networks in terms of a latent
community structure, often yielding intuitions about the underlying social
structure. Typically, this structure is inferred based only on a binary network
representing the presence or absence of interactions between nodes, which
limits the amount of information that can be extracted from the data. In
practice, many interaction networks contain much more information about the
relationship between two nodes. For example, in an email network, the volume of
communication between two users and the content of that communication can give
us information about both the strength and the nature of their relationship.
  In this paper, we propose the Topic Blockmodel, a stochastic blockmodel that
uses a count-based topic model to capture the interaction modalities within and
between latent communities. By explicitly incorporating information sent
between nodes in our network representation, we are able to address questions
of interest in real-world situations, such as predicting recipients for an
email message or inferring the content of an unopened email. Further, by
considering topics associated with a pair of communities, we are better able to
interpret the nature of each community and the manner in which it interacts
with other communities.
