Estimating the expected value of a graph statistic is an important inference task for using and learning graph models. This note presents a scalable estimation procedure for expected motif counts, a widely used type of graph statistic. The procedure applies for generative mixture models of the type used in neural and Bayesian approaches to graph data.

翻译：估计图形统计量的期望值是使用和学习图形模型的重要推断任务。本文介绍了一种可伸缩的估计过程，用于预期相同子图计数，这是一种广泛使用的图形统计量类型。该过程适用于所述神经和贝叶斯方法中使用的生成混合模型。