  Within network data analysis, bipartite networks represent a particular type
of network where relationships occur between two disjoint sets of nodes,
formally called sending and receiving nodes. In this context, sending nodes may
be organized into layers on the basis of some defined characteristics,
resulting in a special case of multilayer bipartite network, where each layer
includes a specific set of sending nodes. To perform a clustering of sending
nodes in multi-layer bipartite network, we extend the Mixture of Latent Trait
Analyzers (MLTA), also taking into account the influence of concomitant
variables on clustering formation and the multi-layer structure of the data. To
this aim, a multilevel approach offers a useful methodological tool to properly
account for the hierarchical structure of the data and for the unobserved
sources of heterogeneity at multiple levels. A simulation study is conducted to
test the performance of the proposal in terms of parameters' and clustering
recovery. Furthermore, the model is applied to the European Social Survey data
(ESS) to i) perform a clustering of individuals (sending nodes) based on their
digital skills (receiving nodes); ii) understand how socio-economic and
demographic characteristics influence the individual digitalization level; iii)
account for the multilevel structure of the data; iv) obtain a clustering of
countries in terms of the base-line attitude to digital technologies of their
residents.
