Research map / Graph learning / Graph self-supervised learning
G-Mixup: Graph Data Augmentation for Graph Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add G-Mixup | GCN · IMDB-B | accuracy: 72.83 -> 72.87 | No clear effect |
| Add G-Mixup | GCN · IMDB-M | accuracy: 48.79 -> 51.30 | Helped |
| Add G-Mixup | GCN · REDD-B | accuracy: 78.82 -> 89.81 | Helped |
| Add G-Mixup | GCN · REDD-M5 | accuracy: 45.07 -> 51.51 | Helped |
| Add G-Mixup | GCN · REDD-M12 | accuracy: 46.90 -> 48.06 | Helped |
| Add G-Mixup | GIN · IMDB-B | accuracy: 72.20 -> 71.94 | Hurt |
| Add G-Mixup | GIN · IMDB-M | accuracy: 48.83 -> 50.46 | Helped |
| Add G-Mixup | GIN · REDD-B | accuracy: 92.59 -> 92.90 | No clear effect |
| Add G-Mixup | GMT · D&D | accuracy: 78.29 -> 79.57 | Helped |
| Add G-Mixup | GMT · MUTAG | accuracy: 82.77 -> 84.44 | Helped |
Closest papers by meaning
- AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification (AAAI 2025 · similarity 0.91)
- Graph Mixup on Approximate Gromov–Wasserstein Geodesics (ICML 2024 · similarity 0.90)
- IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors (NeurIPS 2024 · similarity 0.89)
- Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization (AAAI 2024 · similarity 0.88)
- Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation (ICML 2025 · similarity 0.87)
- Fine-Tuning Graph Neural Networks by Preserving Graph Generative Patterns (AAAI 2024 · similarity 0.87)
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