  Multidimensional network data can have different levels of complexity, as
nodes may be characterized by heterogeneous individual-specific features, which
may vary across the networks. This paper introduces a class of models for
multidimensional network data, where different levels of heterogeneity within
and between networks can be considered. The proposed framework is developed in
the family of latent space models, and it aims to distinguish symmetric
relations between the nodes and node-specific features. Model parameters are
estimated via a Markov Chain Monte Carlo algorithm. Simulated data and an
application to a real example, on fruits import/export data, are used to
illustrate and comment on the performance of the proposed models.
