Network Denoising Revisited: A Ricci-Flow-Inspired Graph Diffusion Method
Networks provide a fundamental representation of relationships among entities. However, real-world networks are often corrupted by noise caused by measurement errors and inherent stochasticity, hindering the discovery of meaningful structure. Most denoising methods rely on similarity-driven diffusion and ignore the non-Euclidean geometry of graphs, where local variations induce heterogeneous information transport. This motivates a geometric revisit of network denoising. In this work, we propose Ricci-Diffusion, a curvature-guided graph diffusion method inspired by Ricci flow. Specifically, Ricci-Diffusion exhibits a Ricci-flow-like evolution, in which relative edge-level curvature modulat...
| Authors | Ye Fang, Chuan-Xian Ren |
|---|---|
| Primary category | cs.SI |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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