  This chapter investigates the latent structure of bipartite networks via a
model-based clustering approach which is able to capture both latent groups of
sending nodes and latent variability of the propensity of sending nodes to
create links with receiving nodes within each group. This modelling approach is
very flexible and can be estimated by using fast inferential approaches such as
variational inference. We apply this model to the analysis of a terrorist
network in order to identify the main latent groups of terrorists and their
latent trait scores based on their attendance to some events.
