Actor-event data are common in sociological settings, whereby one registers
the pattern of attendance of a group of social actors to a number of events. We
focus on 79 members of the Noordin Top terrorist network, who were monitored
attending 45 events. The attendance or non-attendance of the terrorist to
events defines the social fabric, such as group coherence and social
communities. The aim of the analysis of such data is to learn about the
affiliation structure. Actor-event data is often transformed to actor-actor
data in order to be further analysed by network models, such as stochastic
block models. This transformation and such analyses lead to a natural loss of
information, particularly when one is interested in identifying, possibly
overlapping, subgroups or communities of actors on the basis of their
attendances to events. In this paper we propose an actor-event model for
overlapping communities of terrorists, which simplifies interpretation of the
network. We propose a mixture model with overlapping clusters for the analysis
of the binary actor-event network data, called {\tt manet}, and develop a
Bayesian procedure for inference. After a simulation study, we show how this
analysis of the terrorist network has clear interpretative advantages over the
more traditional approaches of affiliation network analysis.Comment: 24 pages, 5 figures; related R package (manet) available on CRA