Research map / Graph learning / Graph algorithms and clustering
CGD: Multi-View Clustering via Cross-View Graph Diffusion
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| cross-view graph diffusion (CGD) vs best multi-view baseline | graph-based multi-view clustering · 3sources | ACC: 0.749 -> 0.781 | Helped |
| CGD vs best baseline | multi-view clustering · Scene-15 | NMI: 0.384 -> 0.419 | Helped |
| CGD vs MLAN | multi-view clustering · BBCSport | ACC: 0.971 -> 0.974 | No clear effect |
| CGD vs GMC (graph fusion baseline) | multi-view clustering · MSRCV1 | ACC: 0.895 -> 0.910 | Helped |
| increase diffusion iterations (ablation) | CGD · MSRCV1 | NMI/ACC | Helped |
Closest papers by meaning
- Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering (CVPR 2025 · similarity 0.92)
- A Non-parametric Graph Clustering Framework for Multi-View Data (AAAI 2024 · similarity 0.91)
- Incomplete and Unpaired Multi-View Graph Clustering with Cross-View Feature Fusion (AAAI 2025 · similarity 0.90)
- Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering (AAAI 2024 · similarity 0.90)
- Incomplete Multi-view Clustering via Diffusion Contrastive Generation (AAAI 2025 · similarity 0.88)
- Global Graph Propagation with Hierarchical Information Transfer for Incomplete Contrastive Multi-view Clustering (AAAI 2025 · similarity 0.87)
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