Research map / Graph learning / GNN architectures
Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add semi-supervised contrastive loss (ablation) | CG³ · Cora | accuracy: 79.2±0.7 -> 83.4±0.7 | Helped |
| Add semi-supervised contrastive loss (ablation) | CG³ · CiteSeer | accuracy: 69.8±1.3 -> 73.6±0.8 | Helped |
| Add semi-supervised contrastive loss (ablation) | CG³ · PubMed | accuracy: 76.6±1.0 -> 80.2±0.8 | Helped |
| Add graph generative loss (ablation) | CG³ · Cora | accuracy: 82.9±0.9 -> 83.4±0.7 | Helped |
| Add graph generative loss (ablation) | CG³ · CiteSeer | accuracy: 72.9±0.9 -> 73.6±0.8 | Helped |
| Add graph generative loss (ablation) | CG³ · PubMed | accuracy: 79.8±0.9 -> 80.2±0.8 | Helped |
Closest papers by meaning
- Semi-Supervised Learning With Graph Learning-Convolutional Networks (CVPR 2019 · similarity 0.92)
- Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017 · similarity 0.91)
- Matrix Completion for Graph-Based Deep Semi-Supervised Learning (AAAI 2019 · similarity 0.91)
- S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning (ICML 2024 · similarity 0.88)
- Graph Structure Refinement with Energy-based Contrastive Learning (AAAI 2025 · similarity 0.88)
- Deep Contrastive Graph Learning with Clustering-Oriented Guidance (AAAI 2024 · similarity 0.87)
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