Graph generative model evaluation necessitates understanding differences between graphs on the distributional level. This entails being able to harness salient attributes of graphs in an efficient manner. Curvature constitutes one such property that has recently proved its utility in characterising graphs. Its expressive properties, stability, and practical utility in model evaluation remain largely unexplored, however. We combine graph curvature descriptors with emerging methods from topological data analysis to obtain robust, expressive descriptors for evaluating graph generative models.

翻译：图生成模型评估需要在分布层面上理解图之间的差异，这要求能够高效利用图的显著属性。曲率作为此类属性之一，近年来在刻画图特性方面展现出实用性，但其表达能力、稳定性以及在模型评估中的实际效用仍待深入探索。我们通过将图曲率描述符与新兴的拓扑数据分析方法相结合，构建了用于评估图生成模型的鲁棒且具表达力的描述符。