Information-geometric adaptive sampling for graph diffusion
Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of distributional evolution on complex manifolds. In this paper, we present an information-geometric framework that reinterprets the diffusion sampling trajectory as a parametric curve on a Riemannian manifold. Our key observation is that the Fisher-Rao metric provides a principled measure of the intrinsic distance. By analyzing this metric, we derive the Drift Variation Score (DVS), a geometry-aware indicator that quantifies the instantaneous rate of distributional change. Unlike prior heuristic-based adaptive samplers, our DVS solver enforces a ...
| Authors | Yuhui Lu, Wenjing Liu, Kun Zhan |
|---|---|
| Primary category | stat.ML |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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