Research map / Graph learning / Graph algorithms and clustering
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use the pseudoinverse-preserving probabilistic reduction (reweight/delete) instead of Spielman-Srivastava effective-resistance sparsification | graph sparsification · three real-world graph datasets | error in Laplacian pseudoinverse action on eigenvectors (d_x) | Helped |
| use the unified deletion/contraction/reweight algorithm instead of existing coarsening algorithms | graph coarsening · three real-world graph datasets | preservation of global structure (d_x) | Helped |
Closest papers by meaning
- Taxonomy of reduction matrices for Graph Coarsening (NeurIPS 2025 · similarity 0.90)
- Structure-Aware Spectral Sparsification via Uniform Edge Sampling (NeurIPS 2025 · similarity 0.89)
- Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks (NeurIPS 2026 · similarity 0.89)
- UGC: Universal Graph Coarsening (NeurIPS 2024 · similarity 0.88)
- Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification (ICML 2025 · similarity 0.88)
- Spectral Graph Pruning Against Over-Squashing and Over-Smoothing (NeurIPS 2024 · similarity 0.87)
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