We propose a weighted stochastic block model (WSBM) which extends the stochastic block model to the important case in which edges are weighted. We address the parameter estimation of the WSBM by use of maximum likelihood and variational approaches, and establish the consistency of these estimators. The problem of choosing the number of classes in a WSBM is addressed. The proposed model is applied to simulated data and an illustrative data set.

中文翻译：

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加权随机块模型

我们提出了一种加权随机块模型（WSBM），它将随机块模型扩展到边缘加权的重要情况。我们通过使用最大似然和变分方法来解决 WSBM 的参数估计，并建立这些估计量的一致性。解决了在 WSBM 中选择类数的问题。所提出的模型适用于模拟数据和说明性数据集。