Social network data are relational data recorded among a group of actors, interacting in different contexts. Often, the same set of actors can be characterized 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 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 nonparametric 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 simulated data experiments, and it is employed to investigate the presence of communities in two multidimensional networks recording relationships of different types among colleagues.
翻译:社会网络数据是一组不同背景的行为者之间记录的关系数据; 通常,同一组行为者的特征是多重社会关系,由多层面网络收集; 一个共同的情况是在同一机构工作的同事,他们的社会互动可以按专业和个人水平加以界定; 此外,网络中的个人往往更经常地与类似的其他人互动,自然地创建社区; 网络数据的原始空间模型有助于恢复行为者的集群,因为它们能够代表他们的位置和相对距离在可解释的低维度社会空间中的相似性; 我们为多维网络数据提出了无限的潜在位置群集模型,使基于模型的行为者组合能够跨越多个社会层面进行互动; 该模型以巴耶斯非对立框架为基础,允许对集群分配、集群数量和潜在社会空间进行自动推断; 该方法通过模拟数据实验进行测试,用于调查两个多维网络中的社区存在情况,记录不同类型同事之间的关系。