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：我们提出并分析一个新的统计程序,即“AgraSSt”,以评估可能无法以明确形式提供的图形生成器的质量。特别是,“AgraSSt” 可用于确定所学的图形生成程序是否能够生成与某一输入图相似的图形。 Stein 操作员为随机图形所启发的 AgraSSt 的关键理念是,根据从图形生成器获得的操作员来构建内核差异。“AgraSSt” 可以为图形生成器培训程序提供可解释的批评,并帮助确定下游任务的可靠样本批次。我们使用Stein's 方法为一系列广泛的随机图形模型提供理论保证。我们提供了具有已知图形生成程序的合成输入图和用于图表的最先进的(深层)基因化模型所训练的“真实世界输入图”的经验结果。