We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators that may not be available in explicit form. In particular, AgraSSt can be used to determine whether a learnt graph generating process is capable of generating graphs that resemble a given input graph. Inspired by Stein operators for random graphs, the key idea of AgraSSt is the construction of a kernel discrepancy based on an operator obtained from the graph generator. AgraSSt can provide interpretable criticisms for a graph generator training procedure and help identify reliable sample batches for downstream tasks. Using Stein`s method we give theoretical guarantees for a broad class of random graph models. We provide empirical results on both synthetic input graphs with known graph generation procedures, and real-world input graphs that the state-of-the-art (deep) generative models for graphs are trained on.

翻译：我们提出并分析了一种新颖的统计方法，命名为AgraSSt，用于评估可能无法以显式形式获取的图生成器的质量。特别地，AgraSSt可用于判断一个学习到的图生成过程是否能够生成与给定输入图相似的图。受随机图斯坦算子启发，AgraSSt的核心思想是基于从图生成器获得的算子构建一个核差异度量。AgraSSt能够为图生成器训练过程提供可解释的批评性意见，并有助于为下游任务识别可靠的样本批次。利用斯坦方法，我们为一类广泛的随机图模型提供了理论保证。我们在已知图生成过程的合成输入图以及当前最先进（深度）图生成模型所训练的真实世界输入图上均提供了实证结果。