Model-based clustering for multidimensional social networks
提出一种无限混合潜在位置聚类模型，用于分析多维社交网络数据，自动推断聚类分配、簇数和潜在社交空间，并通过模拟和实际工作场所网络验证。
Abstract Social network data are relational data recorded among a group of actors, interacting in different contexts. Often, the same set of actors can be characterised by multiple social relations, captured by a multidimensional network. A common situation is that of colleagues working in the same institution, whose social interactions can be defined on professional and personal levels. In addition, individuals in a network tend to interact more frequently with similar others, naturally creating communities. Latent space models for network data are useful to recover clustering of the actors, as they allow to represent similarities between them by their positions and relative distances in an interpretable low-dimensional social space. We propose the infinite mixture latent position cluster model for multidimensional network data, which enables model-based clustering of actors interacting across multiple social dimensions. The model is based on a Bayesian non-parametric framework that allows to perform automatic inference on the clustering allocations, the number of clusters, and the latent social space. The method is tested on extensive simulated data experiments. It is also employed to investigate the presence of communities in two multidimensional workplace social networks recording relations of different types among colleagues.