Research map / Graph learning / GNN architectures
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
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 edge sampling and neuron pruning | GCN · Citeseer | test error: 30.4% -> 28.7% with fewer training nodes (96 vs 120) | Helped |
| joint edge sampling and neuron pruning | GCN · Cora | test error | Helped |
| magnitude-based neuron pruning (ablation) | two-layer GNN on synthetic data · synthetic graph | iterations to zero generalization error | Helped |
| random neuron pruning instead of magnitude pruning (ablation) | two-layer GNN on synthetic data · synthetic graph | iterations to zero generalization error | Hurt |
| importance sampling of class-relevant nodes (larger alpha) (ablation) | two-layer GNN on synthetic data · synthetic graph | iterations to zero generalization error | Helped |
| larger importance sampling probability alpha (ablation) | two-layer GNN on synthetic data · synthetic graph | sample complexity | Helped |
| joint edge sampling and pruning (increasing either alpha or beta) (ablation) | two-layer GNN on synthetic data · synthetic graph | learning success rate | Helped |
| increasing pruning rate beta up to a threshold (ablation) | two-layer GNN on synthetic data · synthetic graph | sample complexity | Helped |
Closest papers by meaning
- Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness (ICML 2024 · similarity 0.91)
- Graph Sparsification via Mixture of Graphs (ICLR 2025 · similarity 0.89)
- On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks (ICML 2025 · similarity 0.89)
- Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks (ICLR 2024 · similarity 0.89)
- A Manifold Perspective on the Statistical Generalization of Graph Neural Networks (ICML 2025 · similarity 0.89)
- Unifews: You Need Fewer Operations for Efficient Graph Neural Networks (ICML 2025 · similarity 0.88)
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.