Papers › Curvature Filtrations for Graph Generative Model Evaluation

Curvature Filtrations for Graph Generative Model Evaluation

30 Jan 2023NeurIPS 2023 11arXiv:2301.12906archive 2025-07-28

Joshua Southern, Jeremy Wayland, Michael Bronstein, Bastian Rieck

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.

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