Research map / Graph learning / Graph self-supervised learning
Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SGDMC instead of the strongest listed baseline, SDMVC | Multi-view clustering · BDGP | ACC (%): 98.78(0.15) vs 97.89(0.52) | Helped |
| Use SGDMC instead of the strongest listed baseline, SDMVC | Multi-view clustering · Handwritten Numerals | ACC (%): 98.24(0.17) vs 97.18(0.51) | Helped |
| Use SGDMC instead of the strongest listed baseline, SDMVC | Multi-view clustering · Reuters | ACC (%): 60.08(1.11) vs 47.07(0.79) | Helped |
| Vary the number of kNN neighbors β (ablation) | SGDMC · BDGP | Clustering performance | Mixed |
| Vary the Gaussian-kernel control parameter γ (ablation) | SGDMC · BDGP | Clustering performance | No clear effect |
Closest papers by meaning
- SURER: Structure-Adaptive Unified Graph Neural Network for Multi-View Clustering (AAAI 2024 · similarity 0.89)
- Multi-View Graph Clustering via Node-Guided Contrastive Encoding (ICML 2025 · similarity 0.88)
- Partial Multi-View Clustering via Self-Supervised Network (AAAI 2024 · similarity 0.88)
- Global Graph Propagation with Hierarchical Information Transfer for Incomplete Contrastive Multi-view Clustering (AAAI 2025 · similarity 0.88)
- Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering (AAAI 2024 · similarity 0.87)
- EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering (CVPR 2025 · similarity 0.87)
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