Research map / Graph learning / GNN architectures
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the GCN-inspired graph kernel instead of GCN | GCNGP · Cora | Micro-F1: 0.8183\se{0.0055} -> 0.8280 | Helped |
| Use the GCN-inspired graph kernel instead of GCN | GCNGP · Citeseer | Micro-F1: 0.6941\se{0.0079} -> 0.7090 | Helped |
| Use the GCN-inspired graph kernel instead of GCN | GCNGP · PubMed | Micro-F1: 0.7649\se{0.0058} -> 0.7960 | Helped |
| Use the low-rank graph-kernel approximation instead of GCN | GCNGP-X · ArXiv | Micro-F1: 0.6990\se{0.0014} -> 0.7011\se{0.0011} | Helped |
| Use the low-rank graph-kernel approximation instead of GCN | GCNGP-X · Reddit | Micro-F1: 0.9330\se{0.0006} -> 0.9465\se{0.0003} | Helped |
| Use the GCN-inspired graph kernel instead of GCN | GCNGP · Chameleon | R^2: 0.5690\se{0.0376} -> 0.6720 | Helped |
| Use the GCN-inspired graph kernel instead of GCN | GCNGP · Squirrel | R^2: 0.4243\se{0.0393} -> 0.4926 | Helped |
| Use the GCN-inspired graph kernel instead of GCN | GCNGP · R^2 | R^2: 0.6976\se{0.0323} -> 0.8002 | Helped |
| Apply the low-rank approximation to the full graph kernel (ablation) | GCNGP · Cora | Micro-F1: 0.8280 -> 0.7980 | Hurt |
| Apply the low-rank approximation to the full graph kernel (ablation) | GCNGP · Chameleon | R^2: 0.6720 -> 0.6852 | Helped |
| Use a graph-aware GCNGP kernel instead of RBF | GCNGP · Cora | Micro-F1: 0.5860 -> 0.8280 | Helped |
| Use the matching GP kernel instead of the GNN | GCNII-GP · PubMed | Micro-F1: 0.7558\se{0.0096} -> 0.7840 | Helped |
Closest papers by meaning
- Bayesian Semi-supervised Learning with Graph Gaussian Processes (NeurIPS 2018 · similarity 0.91)
- Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks (ICML 2024 · similarity 0.89)
- How Graph Neural Networks Learn: Lessons from Training Dynamics (ICML 2024 · similarity 0.88)
- On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks (ICML 2025 · similarity 0.87)
- Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective (ICLR 2025 · similarity 0.87)
- A Manifold Perspective on the Statistical Generalization of Graph Neural Networks (ICML 2025 · similarity 0.87)
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