Research map / Graph learning / Graph algorithms and clustering
The Geometric Block Model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use motif-counting instead of spectral clustering | Community detection · Political Blogs | accuracy: 0.53 -> 0.788 | Helped |
| Use motif-counting instead of spectral clustering | Community detection · DBLP | accuracy: 0.63 -> 0.675 | Helped |
| Use motif-counting instead of spectral clustering | Community detection · LiveJournal | accuracy: 0.64 -> 0.7768 | Helped |
| Use motif-counting instead of spectral clustering | Community detection · Political Blogs | running time (sec): 0.29 -> 1.62 | Hurt |
| Use motif-counting instead of spectral clustering | Community detection · DBLP | running time (sec): 18.077 -> 3.93 | Helped |
| Use motif-counting instead of spectral clustering | Community detection · LiveJournal | running time (sec): 1.54 -> 0.49 | Helped |
| Use common-neighbor Motif 1 rather than Motif 2 or Motif 4 (ablation) | GBM motif-counting algorithm · GBM theoretical guarantee comparison | minimum a guaranteeing success | Helped |
| Use triangle counting for SBM in the sparse average-degree regime | SBM community detection · SBM sparse regime | recovery regime: p,q ∼ √(log n/n) | Hurt |
Closest papers by meaning
- Active Learning in the Geometric Block Model (AAAI 2020 · similarity 0.93)
- Exact Community Recovery under Side Information: Optimality of Spectral Algorithms (ICLR 2025 · similarity 0.88)
- Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model (ICML 2025 · similarity 0.87)
- Differentially private exact recovery for stochastic block models (ICML 2024 · similarity 0.87)
- Optimal community detection in dense bipartite graphs (NeurIPS 2025 · similarity 0.87)
- Efficient Graph Matching for Correlated Stochastic Block Models (NeurIPS 2024 · similarity 0.86)
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