论文标题
通过随机块模型中的简单超图中的基于模型的聚类
Model-based clustering in simple hypergraphs through a stochastic blockmodel
论文作者
论文摘要
我们提出了一个模型，以解决简单超图中的节点聚类的被忽视问题。当节点可能不会在同一Hyperdeed中多次出现时，例如在共同授权数据集中，简单的超图很适合。我们的模型概括了图形的随机块模型，并假设存在潜在节点组的存在，并且鉴于这些基团，有条件地是独立的。我们首先建立模型参数的通用可识别性。然后，我们为参数推理和节点聚类开发了变分近似期望最大化算法，并得出模型选择的统计标准。 为了说明我们的R软件包HyperSBM的性能，我们使用从模型生成的合成数据以及线群集实验和共同创作数据集将其与其他节点聚类方法进行了比较。
We propose a model to address the overlooked problem of node clustering in simple hypergraphs. Simple hypergraphs are suitable when a node may not appear multiple times in the same hyperedge, such as in co-authorship datasets. Our model generalizes the stochastic blockmodel for graphs and assumes the existence of latent node groups and hyperedges are conditionally independent given these groups. We first establish the generic identifiability of the model parameters. We then develop a variational approximation Expectation-Maximization algorithm for parameter inference and node clustering, and derive a statistical criterion for model selection. To illustrate the performance of our R package HyperSBM, we compare it with other node clustering methods using synthetic data generated from the model, as well as from a line clustering experiment and a co-authorship dataset.
