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Southern, J.* ; Wayland, J.D. ; Bronstein, M.D.* ; Rieck, B.

Curvature Filtrations for Graph Generative Model Evaluation.

In: (37th Conference on Neural Information Processing Systems (NeurIPS), 10-16 December 2023, New Orleans, LA). 10010 North Torrey Pines Rd, La Jolla, California 92037 Usa: Neural Information Processing Systems (nips), 2023. 26

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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.