Research map / Graph learning / Graph algorithms and clustering
Featured Graph Coarsening with Similarity Guarantees
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 |
|---|---|---|---|
| Joint learning of coarsened graph and features (FGC) with sparsity regularizer vs. SCAL/GCOND | FGC · Node classification, Cora r=0.3 | accuracy: 85.79 vs 81.56 (GCOND) and 79.42 (SCAL) | Helped |
| Replacing GCN with GAT/APPNP as downstream GNN on FGC coarsened graphs (ablation) | FGC · Node classification, Cora/Citeseer/Co-phy/Pubmed | accuracy: similar accuracy across GCN, GAT, APPNP | No clear effect |
| Feature-aware coarsening (FGC) vs. structure-only coarsening (LVN/LVE) on spectral and REE metrics | FGC · Cora r=0.3, REE | REE: 0.04 vs 0.65/0.71 | Helped |
Closest papers by meaning
- Graph Coarsening with Neural Networks (ICLR 2021 · similarity 0.93)
- Graph Coarsening with Message-Passing Guarantees (NeurIPS 2024 · similarity 0.90)
- Taxonomy of reduction matrices for Graph Coarsening (NeurIPS 2025 · similarity 0.90)
- UGC: Universal Graph Coarsening (NeurIPS 2024 · similarity 0.88)
- Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training (AAAI 2025 · similarity 0.88)
- Graph Sparsification via Mixture of Graphs (ICLR 2025 · similarity 0.87)
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.