Research map / Learning theory and algorithms / Clustering
Clustering Signed Networks with the Geometric Mean of Laplacians
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the geometric-mean Laplacian instead of the strongest listed baseline, L_SN | Signed spectral clustering · iris | fraction of cases best: 23.4 -> 59.4 | Helped |
| Use the geometric-mean Laplacian instead of the strongest listed baseline, L_SN | Signed spectral clustering · wine | fraction of cases best: 40.6 -> 42.2 | Helped |
| Use the geometric-mean Laplacian instead of the strongest listed baseline, L_SN | Signed spectral clustering · ecoli | fraction of cases best: 18.8 -> 65.6 | Helped |
| Use the geometric-mean Laplacian instead of the strongest listed baseline, L_SN | Signed spectral clustering · optdig | fraction of cases best: 28.1 -> 71.9 | Helped |
| Use the geometric-mean Laplacian instead of the strongest listed baseline, L_SN | Signed spectral clustering · USPS | fraction of cases best: 10.9 -> 89.1 | Helped |
| Use the geometric-mean Laplacian instead of the strongest listed baseline, L_SN | Signed spectral clustering · pendig | fraction of cases best: 10.9 -> 84.4 | Helped |
| Use the geometric-mean Laplacian instead of the strongest listed baseline, L_SN | Signed spectral clustering · MNIST | fraction of cases best: 12.5 -> 87.5 | Helped |
Closest papers by meaning
- Spectral Clustering of Signed Graphs via Matrix Power Means (ICML 2019 · similarity 0.93)
- Signed Laplacians for Constrained Graph Clustering (ICML 2025 · similarity 0.90)
- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.86)
- Structure-Aware Spectral Sparsification via Uniform Edge Sampling (NeurIPS 2025 · similarity 0.86)
- Optimal Graph Clustering without Edge Density Signals (NeurIPS 2025 · similarity 0.85)
- A Differentially Private Clustering Algorithm for Well-Clustered Graphs (ICLR 2024 · similarity 0.84)
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