多维社交网络的基于模型的聚类

Silvia D’Angelo, Marco Alfò, Michael Fop
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引用次数: 1

摘要

社交网络数据是一组参与者在不同情境下相互作用的关系数据。通常,同一组行为者可以具有多种社会关系的特征,由多维网络捕获。一种常见的情况是在同一机构工作的同事,他们的社会互动可以在专业和个人层面上定义。此外,网络中的个人倾向于更频繁地与相似的人互动,自然地创造了社区。网络数据的潜在空间模型对于恢复参与者的聚类很有用,因为它们允许通过他们在可解释的低维社会空间中的位置和相对距离来表示他们之间的相似性。我们提出了多维网络数据的无限混合潜在位置聚类模型,该模型实现了跨多个社会维度交互行为者的基于模型的聚类。该模型基于贝叶斯非参数框架,允许对聚类分配、聚类数量和潜在社会空间进行自动推理。通过大量的模拟数据实验对该方法进行了验证。本研究还探讨了记录不同类型同事之间关系的两个多维职场社交网络中社区的存在。
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Model-based clustering for multidimensional social networks
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.
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