English

Sobolev--Ricci Curvature

Machine Learning 2026-03-16 v1

Abstract

Ricci curvature is a fundamental concept in differential geometry for encoding local geometric structure, and its graph-based analogues have recently gained prominence as practical tools for reweighting, pruning, and reshaping network geometry. We propose Sobolev-Ricci Curvature (SRC), a graph Ricci curvature canonically induced by Sobolev transport geometry, which admits efficient evaluation via a tree-metric Sobolev structure on neighborhood measures. We establish two consistency behaviors that anchor SRC to classical transport curvature: (i) on trees endowed with the length measure, SRC recovers Ollivier-Ricci curvature (ORC) in the canonical W1 setting, and (ii) SRC vanishes in the Dirac limit, matching the flat case of measure-theoretic Ricci curvature. We demonstrate SRC as a reusable curvature primitive in two representative pipelines. We define Sobolev-Ricci Flow by replacing ORC with SRC in a Ricci-flow-style reweighting rule, and we use SRC for curvature-guided edge pruning aimed at preserving manifold structure. Overall, SRC provides a transport-based foundation for scalable curvature-driven graph transformation and manifold-oriented pruning.

Cite

@article{arxiv.2603.12652,
  title  = {Sobolev--Ricci Curvature},
  author = {Kyoichi Iwasaki and Tam Le and Hideitsu Hino},
  journal= {arXiv preprint arXiv:2603.12652},
  year   = {2026}
}

Comments

42 pages, 13 figures

R2 v1 2026-07-01T11:17:53.810Z