Research map / Graph learning / GNN architectures
Graph Transformer Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn task-specific meta-path graphs end-to-end instead of using a homogeneous GAT graph | GTN · DBLP | F1 score: 93.71 -> 94.18 | Helped |
| learn task-specific meta-path graphs end-to-end instead of using a homogeneous GAT graph | GTN · ACM | F1 score: 92.33 -> 92.68 | Helped |
| learn task-specific meta-path graphs end-to-end instead of using a homogeneous GAT graph | GTN · IMDB | F1 score: 58.14 -> 60.92 | Helped |
| add an identity matrix to candidate adjacency matrices, enabling shorter and variable-length meta-paths (ablation) | GTN · DBLP | F1 score: 93.91 -> 94.18 | Helped |
| add an identity matrix to candidate adjacency matrices, enabling shorter and variable-length meta-paths (ablation) | GTN · ACM | F1 score: 91.13 -> 92.68 | Helped |
| add an identity matrix to candidate adjacency matrices, enabling shorter and variable-length meta-paths (ablation) | GTN · IMDB | F1 score: 52.33 -> 60.92 | Helped |
Closest papers by meaning
- NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification (NeurIPS 2022 · similarity 0.92)
- Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence (ICML 2025 · similarity 0.91)
- Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification (NeurIPS 2024 · similarity 0.91)
- Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-Based Similarity (AAAI 2024 · similarity 0.90)
- NLGT: Neighborhood-based and Label-enhanced Graph Transformer Framework for Node Classification (AAAI 2025 · similarity 0.89)
- Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers (NeurIPS 2024 · similarity 0.88)
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