Geometric Structures on Graphs: a Holonomy-Based Discretization of Curvature
Not on the map. Why?
Abstract (arXiv)
We propose a holonomy-based framework for discretizing curvature on graphs equipped with local symmetric positive-definite metrics. Each vertex carries a fibre metric (gi), and each directed edge carries a reversible metric-compatible transport (Fij). The ordered product around an oriented triangular loop (mathcal C) gives a holonomy (Hmathcal C), whose normalized logarithm (Omegamathcal C=-smathcal C-1operatornameLog(Hmathcal C)) is used as a finite-loop curvature observation. Thus the construction discretizes the geometric principle that infinitesimal holonomy is controlled by curvature, rather than treating holonomy as a heuristic feature. Since (Omegamathcal C) lies in the (gi)-orthogonal Lie algebra, it is not itself a velocity of an SPD metric. We therefore introduce two aggregation mechanisms: a commutator with a symmetric response matrix, producing symmetric Ricci-type metric responses, and an incidence-aware covariant divergence of curvature-induced edge fluxes, reflecting the relation between trace and covariant divergence. The resulting responses are locally orthogonal-gauge equivariant and can drive exponential updates that preserve positive definiteness. We also give a reversible metric-compatible parametrization of edge transports, allowing orthogonal edge factors, loop scales, weights, and response matrices to be learned while respecting the graph geometry. Known-geometry calibrations on the unit sphere test the holonomy--curvature relation, curvature preservation under nontrivial local metric representations, and the empirical recovery of edge transports from local observations.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 23 Aug 2026.
Builds on
5 of 13 references.
References · 5
- 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 478
- 2019Gauge Equivariant Convolutional Networks and the Icosahedral CNNcited by 267
- 2020Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphscited by 92
- 2019Community Detection on Networks with Ricci Flowcited by 62
- 2021Curvature Graph Neural Networkcited by 24
Built on by
Not cited yet.