论文标题
随机块光滑的图形模型
Stochastic Block Smooth Graphon Model
论文作者
论文摘要
本文提出了随机块模型与光滑的Graphon模型的组合。首先允许将网络中的个体集划分为块，这些块代表大概是随机连接的一组节点，因此通常也称为社区。相反，平滑的Graphon模型假设网络的节点可以以一维量表的形式排列，从而使紧密度意味着相似的连通性行为。这两个模型都属于节点特异性潜在变量的模型类别，需要自然关系。尽管这些模型链或多或少地独立发展，但本文提出了它们对随机块平滑图形模型的概括。这种方法能够利用两者的优势。我们追求一种一般的EM-Type算法进行估计，并通过将模型应用于模拟和现实世界的示例来证明可用性。
The paper proposes the combination of stochastic blockmodels with smooth graphon models. The first allow for partitioning the set of individuals in a network into blocks which represent groups of nodes that presumably connect stochastically equivalently, therefore often also called communities. Smooth graphon models instead assume that the network's nodes can be arranged on a one-dimensional scale such that closeness implies a similar connectivity behavior. Both models belong to the model class of node-specific latent variables, entailing a natural relationship. While these model strands have developed more or less completely independently, this paper proposes their generalization towards stochastic block smooth graphon models. This approach enables to exploit the advantages of both worlds. We pursue a general EM-type algorithm for estimation and demonstrate the usability by applying the model to both simulated and real-world examples.
