Curvature Filtrations for Graph Generative Model Evaluation
This work addresses the need for better evaluation metrics in graph generation, which is important for researchers and practitioners in machine learning and network science, though it appears incremental as it builds on existing curvature and topological methods.
The paper tackled the problem of evaluating graph generative models by proposing a method that combines graph curvature descriptors with topological data analysis to create robust and expressive descriptors for distribution-level graph comparison.
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.