This work addresses the rising demand for novel tools in statistical and machine learning for "graph-valued random variables" by proposing a fast algorithm to compute the sample Frechet mean, which replaces the concept of sample mean for graphs (or networks). We use convolutional neural networks to learn the morphology of the graphs in a set of graphs. Our experiments on several ensembles of random graphs demonstrate that our method can reliably recover the sample Frechet mean.

翻译：这项工作通过提出快速算法来计算Frechet样本,从而取代图表(或网络)的样本平均值概念。 我们使用进化神经网络来学习一组图表中图表的形态。 我们对几个随机图表组合的实验表明,我们的方法可以可靠地恢复Frechet样本的含义。