Research map / Learning theory and algorithms / Clustering
Spectral Clustering of Signed Graphs via Matrix Power Means
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use smaller power-mean parameter p (ablation) | Signed Power Mean Laplacian · SSBM | clustering error | Helped |
| Use p<0 instead of p>0 (ablation) | Signed Power Mean Laplacian · SSBM with one informative edge type | cluster recovery | Helped |
| Use p=-10 rather than p=10 (ablation) | Signed Power Mean Laplacian · SSBM with G+ informative and G− transitioning from informative to non-informative | clustering error | Helped |
| Use p≤0 rather than the arithmetic-mean case p=1 (ablation) | Signed Power Mean Laplacian · Wikipedia-Elections | clustering structure identified | Helped |
| Use p<0 rather than p=0 (ablation) | Signed Power Mean Laplacian · SSBM | cluster recovery conditions | Helped |
| Use p<0 rather than p>0 (ablation) | Signed Power Mean Laplacian · SSBM with G− informative and G+ transitioning from non-informative to informative | cluster recovery | Helped |
| Replace the arithmetic-mean-like signed Laplacians with a negative-power SPM Laplacian | Signed graph clustering · SSBM | cluster recovery | Helped |
Closest papers by meaning
- Signed Laplacians for Constrained Graph Clustering (ICML 2025 · similarity 0.90)
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- On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models (NeurIPS 2024 · similarity 0.88)
- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.88)
- A Differentially Private Clustering Algorithm for Well-Clustered Graphs (ICLR 2024 · similarity 0.86)
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