论文标题
混合模型和网络 - 随机块改造的概述
Mixture Models and Networks -- Overview of Stochastic Blockmodelling
论文作者
论文摘要
混合模型是概率模型，旨在发现和代表人群中的潜在亚组。在网络数据分析的领域中，节点的潜在亚组通常通过其连接性行为来识别，节点的行为类似属于同一社区。在这种情况下，混合模型是通过随机块改造进行的。我们考虑从混合建模的角度考虑随机块模型及其一些变体和扩展。我们还调查了一些可用的估计方法的主要类别，并提出了另一种方法。除了讨论推论属性和估计程序外，我们还专注于模型在几个现实世界网络数据集中的应用，展示了不同方法的优点和陷阱。
Mixture models are probabilistic models aimed at uncovering and representing latent subgroups within a population. In the realm of network data analysis, the latent subgroups of nodes are typically identified by their connectivity behaviour, with nodes behaving similarly belonging to the same community. In this context, mixture modelling is pursued through stochastic blockmodelling. We consider stochastic blockmodels and some of their variants and extensions from a mixture modelling perspective. We also survey some of the main classes of estimation methods available, and propose an alternative approach. In addition to the discussion of inferential properties and estimating procedures, we focus on the application of the models to several real-world network datasets, showcasing the advantages and pitfalls of different approaches.
