Research map / Graph learning / GNN architectures
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use NAGphormer instead of the strongest listed baseline, GPRGNN | Node classification models · Pubmed | accuracy (%): 89.34 ± 0.25 -> 89.70 ± 0.19 | Helped |
| Use NAGphormer instead of the strongest listed baseline, GRAND+ | Node classification models · CoraFull | accuracy (%): 71.37 ± 0.11 -> 71.51 ± 0.13 | Helped |
| Use NAGphormer instead of the strongest listed baseline, GT | Node classification models · Computer | accuracy (%): 91.18 ± 0.17 -> 91.22 ± 0.14 | No clear effect |
| Use NAGphormer instead of the strongest listed baseline, GraphGPS | Node classification models · Photo | accuracy (%): 95.06 ± 0.13 -> 95.49 ± 0.11 | Helped |
| Use NAGphormer instead of the strongest listed baseline, GPRGNN | Node classification models · CS | accuracy (%): 95.13 ± 0.09 -> 95.75 ± 0.09 | Helped |
| Use NAGphormer instead of the strongest listed baseline, GT | Node classification models · Physics | accuracy (%): 97.05 ± 0.05 -> 97.34 ± 0.03 | Helped |
| Use NAGphormer instead of GRAND+ | Scalable node classification models · AMiner-CS | accuracy (%): 54.67 ± 0.25 -> 56.21 ± 0.42 | Helped |
| Use NAGphormer instead of GRAND+ | Scalable node classification models · Reddit | accuracy (%): 92.81 ± 0.03 -> 93.58 ± 0.05 | Helped |
| Use NAGphormer instead of GRAND+ | Scalable node classification models · Amazon2M | accuracy (%): 75.49 ± 0.11 -> 77.43 ± 0.24 | Helped |
| Add Laplacian structural encoding (ablation) | NAGphormer · Pubmed | accuracy (%): 89.06 -> 89.70; +0.64 | Helped |
| Add Laplacian structural encoding (ablation) | NAGphormer · CoraFull | accuracy (%): 70.42 -> 71.51; +1.09 | Helped |
| Add Laplacian structural encoding (ablation) | NAGphormer · CS | accuracy (%): 95.52 -> 95.75; +0.23 | Helped |
Closest papers by meaning
- Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification (AAAI 2025 · similarity 0.91)
- NLGT: Neighborhood-based and Label-enhanced Graph Transformer Framework for Node Classification (AAAI 2025 · similarity 0.89)
- VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections (ICLR 2024 · similarity 0.88)
- Generative Graph Pattern Machine (NeurIPS 2025 · similarity 0.88)
- Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers (NeurIPS 2024 · similarity 0.88)
- What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding (ICML 2024 · similarity 0.88)
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