  We present a new network model accounting for multidimensional assortativity.
Each node is characterized by a number of features and the probability of a
link between two nodes depends on common features. We do not fix a priori the
total number of possible features. The bipartite network of the nodes and the
features evolves according to a stochastic dynamics that depends on three
parameters that respectively regulate the preferential attachment in the
transmission of the features to the nodes, the number of new features per node,
and the power-law behavior of the total number of observed features. Our model
also takes into account a mechanism of triadic closure. We provide theoretical
results and statistical estimators for the parameters of the model. We validate
our approach by means of simulations and an empirical analysis of a network of
scientific collaborations.
