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：在网络数据分析领域，二部网络是一种特殊类型的网络，其连接关系存在于两个互不相交的节点集之间，这两个节点集通常被称为发送节点和接收节点。在此背景下，发送节点可根据某些定义特征组织成不同层级，构成多层二部网络的特例——每层包含特定的发送节点集。为对多层二部网络中的发送节点进行聚类，我们扩展了潜在特质分析器混合模型（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，多级方法提供了有效的方法论工具，能够恰当处理数据的层次结构及多级未观测异质性来源。通过仿真研究测试了所提模型在参数恢复与聚类恢复方面的性能。此外，将该模型应用于欧洲社会调查（ESS）数据以：i) 基于个体（发送节点）的数字技能（接收节点）对其聚类；ii) 理解社会经济与人口统计特征如何影响个体数字化水平；iii) 考虑数据的多级结构；iv) 根据居民对数字技术的基础态度实现国家层面的聚类。