Research map / Graph learning / Graph self-supervised learning
Simple Unsupervised Graph Representation Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SUGRL instead of the strongest listed baseline | unsupervised node classification · Cora | accuracy: 83.1 ± 0.2 -> 83.4 ± 0.5 | Helped |
| Use SUGRL instead of the strongest listed baseline | unsupervised node classification · CiteSeer | accuracy: 72.6 ± 0.4 -> 73.0 ± 0.4 | Helped |
| Use SUGRL instead of the strongest listed baseline | unsupervised node classification · PubMed | accuracy: 81.0 ± 0.3 -> 81.9 ± 0.3 | Helped |
| Use SUGRL instead of the strongest listed baseline | unsupervised node classification · Photo | accuracy: 92.4 ± 0.4 -> 93.2 ± 0.4 | Helped |
| Use SUGRL instead of the strongest listed baseline | unsupervised node classification · Computers | accuracy: 85.7 ± 0.1 -> 88.9 ± 0.3 | Helped |
| Use SUGRL instead of the strongest listed baseline | unsupervised node classification · Ogbn-arxiv | accuracy: 87.8 ± 0.2 -> 68.8 ± 0.4 | Mixed |
| Use SUGRL instead of GRACE | unsupervised node classification · Ogbn-mag | accuracy: 31.5 ± 0.3 -> 31.9 ± 0.3 | Helped |
| Use mini-batch strategy | SUGRL on Ogbn-mag · Ogbn-mag | accuracy: 31.9 ± 0.3 -> 32.4 ± 0.1 | Helped |
| Use SUGRL instead of the strongest listed baseline | unsupervised node classification · Ogbn-products | accuracy: 78.4 ± 0.3 -> 82.6 ± 0.4 | Helped |
| Add structural-information loss LS (ablation) | SUGRL on Photo · Photo | accuracy: 74.9 ± 0.4 -> 93.2 ± 0.2 | Helped |
| Add neighbor-information loss LN (ablation) | SUGRL on Photo · Photo | accuracy: 86.9 ± 0.3 -> 93.2 ± 0.2 | Helped |
| Add upper-bound loss LU (ablation) | SUGRL on Photo · Photo | accuracy: 91.6 ± 0.3 -> 93.2 ± 0.2 | Helped |
Closest papers by meaning
- Return to Basics: Very Simple Graph Contrastive Learning via Noise Cancellation Principle (NeurIPS 2026 · similarity 0.91)
- Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling (AAAI 2025 · similarity 0.89)
- Multiplex Graph Representation Learning with Homophily and Consistency (AAAI 2025 · similarity 0.89)
- ReGCL: Rethinking Message Passing in Graph Contrastive Learning (AAAI 2024 · similarity 0.88)
- A New Mechanism for Eliminating Implicit Conflict in Graph Contrastive Learning (AAAI 2024 · similarity 0.88)
- Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering (NeurIPS 2024 · similarity 0.88)
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