Research map / Graph learning / Graph self-supervised learning
Hypergraph p-Laplacian: A Differential Geometry View
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Tune p instead of fixing it to 2 (ablation) | Proposed hypergraph p-Laplacian · Mushroom two-class clustering | error rate: 0.3156 -> 0.2329 | Helped |
| Tune p instead of fixing it to 2 (ablation) | Proposed hypergraph p-Laplacian · Breast Cancer two-class clustering | error rate: 0.0286 -> 0.0243 | Helped |
| Tune p instead of fixing it to 2 (ablation) | Proposed hypergraph p-Laplacian · Chess two-class clustering | error rate: 0.4775 -> 0.2847 | Helped |
| Tune p instead of fixing it to 2 (ablation) | Proposed hypergraph p-Laplacian · Congress two-class clustering | error rate: 0.1241 -> 0.1195 | Helped |
| Use the tuned p-Laplacian rather than Zhou's Laplacian | Hypergraph clustering · Mushroom two-class clustering | error rate: 0.3156 -> 0.2329 | Helped |
| Use the tuned p-Laplacian rather than Zhou's Laplacian | Hypergraph clustering · Breast Cancer two-class clustering | error rate: 0.0300 -> 0.0243 | Helped |
| Use the tuned p-Laplacian rather than Zhou's Laplacian | Hypergraph clustering · Chess two-class clustering | error rate: 0.4925 -> 0.2847 | Helped |
| Use the tuned p-Laplacian rather than Zhou's Laplacian | Hypergraph clustering · Congress two-class clustering | error rate: 0.1241 -> 0.1195 | Helped |
| Use the tuned p-Laplacian rather than Hein's regularizer | Hypergraph clustering · Mushroom two-class clustering | error rate: 0.1349 -> 0.2329 | Hurt |
| Use the tuned p-Laplacian rather than Hein's regularizer | Hypergraph clustering · Breast Cancer two-class clustering | error rate: 0.3362 -> 0.0243 | Helped |
| Use the tuned p-Laplacian rather than Hein's regularizer | Hypergraph clustering · Chess two-class clustering | error rate: 0.4778 -> 0.2847 | Helped |
| Use the tuned p-Laplacian rather than Hein's regularizer | Hypergraph clustering · Congress two-class clustering | error rate: 0.3034 -> 0.1195 | Helped |
Closest papers by meaning
- Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning (ICML 2024 · similarity 0.89)
- Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering (ICML 2018 · similarity 0.88)
- Hypergraph Learning for Unsupervised Graph Alignment via Optimal Transport (AAAI 2025 · similarity 0.87)
- Hypergraph-enhanced Dual Semi-supervised Graph Classification (ICML 2024 · similarity 0.86)
- Hypergraph Joint Representation Learning for Hypervertices and Hyperedges via Cross Expansion (AAAI 2024 · similarity 0.85)
- Geometric Imbalance in Semi-Supervised Node Classification (NeurIPS 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
