  Social network analysis is the study of how links between a set of actors are
formed. Typically, it is believed that links are formed in a structured manner,
which may be due to, for example, political or material incentives, and which
often may not be directly observable. The stochastic blockmodel represents this
structure using latent groups which exhibit different connective properties, so
that conditional on the group membership of two actors, the probability of a
link being formed between them is represented by a connectivity matrix. The
mixed membership stochastic blockmodel (MMSBM) extends this model to allow
actors membership to different groups, depending on the interaction in
question, providing further flexibility.
  Attribute information can also play an important role in explaining network
formation. Network models which do not explicitly incorporate covariate
information require the analyst to compare fitted network models to additional
attributes in a post-hoc manner. We introduce the mixed membership of experts
stochastic blockmodel, an extension to the MMSBM which incorporates covariate
actor information into the existing model. The method is illustrated with
application to the Lazega Lawyers dataset. Model and variable selection methods
are also discussed.
