  In this work we are interested in identifying clusters of "positional
equivalent" actors, i.e. actors who play a similar role in a system. In
particular, we analyze weighted bipartite networks that describes the
relationships between actors on one side and features or traits on the other,
together with the intensity level to which actors show their features. The main
contribution of our work is twofold. First, we develop a methodological
approach that takes into account the underlying multivariate dependence among
groups of actors. The idea is that positions in a network could be defined on
the basis of the similar intensity levels that the actors exhibit in expressing
some features, instead of just considering relationships that actors hold with
each others. Second, we propose a new clustering procedure that exploits the
potentiality of copula functions, a mathematical instrument for the
modelization of the stochastic dependence structure. Our clustering algorithm
can be applied both to binary and real-valued matrices. We validate it with
simulations and applications to real-world data.
