Research map / Graph learning / Graph algorithms and clustering
Learning A Structured Optimal Bipartite Graph for Co-Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn bipartite graph with exactly k connected components instead of spectral clustering plus K-means post-processing | co-clustering (SOBG vs BSGP) · Synthetic rows, noise 0.9 | clustering accuracy (%): 40.00 -> 84.17 | Helped |
| learn bipartite graph with exactly k connected components | co-clustering (SOBG vs BSGP) · Synthetic columns, noise 0.8 | clustering accuracy (%): 63.33 -> 100.00 | Helped |
| learn bipartite graph with exactly k connected components | co-clustering (SOBG vs BSGP) · Synthetic, noise 0.6 | clustering accuracy (%): 100.00 vs 100.00 (rows, BSGP) | No clear effect |
| learn bipartite graph with exactly k connected components | co-clustering (SOBG vs BSGP) · Reuters21578 | clustering accuracy (%): 11.44 -> 43.94 | Helped |
| learn bipartite graph with exactly k connected components | co-clustering (SOBG vs best baseline) · LUNG | clustering accuracy (%): 79.80 -> 78.82 | No clear effect |
| learn bipartite graph with exactly k connected components | co-clustering (SOBG vs NMF) · Prostate-MS | clustering accuracy (%): 47.83 -> 62.73 | Helped |
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- Improved Approximation Algorithms for Chromatic and Pseudometric-Weighted Correlation Clustering (NeurIPS 2025 · similarity 0.85)
- Local Graph Clustering with Noisy Labels (ICLR 2024 · similarity 0.85)
- DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization (AAAI 2024 · similarity 0.85)
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