Cluster analysis of weighted bipartite networks: a new copula-based approach

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. 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 basisof the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Moreover, we propose anew clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clusteringalgorithm can be applied both to binary and real-valued matrices. We validate it withsimulations and applications to real-world data.