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.

翻译：在网络数据分析中，二部网络是一种特殊类型的网络，其关系发生在两个互不相交的节点集之间，形式上称为发送节点和接收节点。在此背景下，发送节点可能根据某些定义特征划分为不同层级，形成多层二部网络的特例，其中每个层级包含一组特定的发送节点。为对多层二部网络中的发送节点进行聚类，我们扩展了潜特质分析混合模型（MLTA），同时考虑了伴随变量对聚类形成的影响以及数据的多层结构。为此，多层次方法为合理刻画数据的层次结构及多层级不可观测异质性来源提供了有效方法论工具。通过仿真实验检验了该模型在参数估计和聚类恢复方面的性能。此外，将该模型应用于欧洲社会调查数据（ESS），旨在实现以下目标：i) 基于个体数字技能（接收节点）对个体（发送节点）进行聚类；ii) 理解社会经济与人口特征如何影响个体数字化水平；iii) 考虑数据的多层次结构；iv) 根据居民对数字技术的基础态度对各国进行聚类。