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：估计图统计量的期望值是对图模型进行推理与学习的重要任务。本文提出了一种适用于期望子图计数的可扩展估计方法——子图计数是广泛使用的图统计量类型。该方法适用于神经方法和贝叶斯方法中采用的生成混合模型。