Research map / Learning theory and algorithms / Clustering
Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Keep each vertex's k highest-ranked positive neighbors in a single pass, then run a Pivot variant on the retained graph | Single-Pass Pivot · Correlation Clustering, complete graphs | approximation factor: (3+ε) versus 5 | Helped |
| Use O(n/ε) words instead of O(n log n) words | Single-Pass Pivot · Correlation Clustering, complete graphs | memory: O(n log n) -> O(n/ε) words | Helped |
Closest papers by meaning
- Correlation Clustering Beyond the Pivot Algorithm (ICML 2025 · similarity 0.93)
- Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models (ICML 2024 · similarity 0.90)
- Sparse-pivot: Dynamic correlation clustering for node insertions (ICML 2025 · similarity 0.88)
- Learning-Augmented Streaming Algorithms for Correlation Clustering (NeurIPS 2025 · similarity 0.88)
- Dynamic Correlation Clustering in Sublinear Update Time (ICML 2024 · similarity 0.85)
- Improved Approximation Algorithms for Chromatic and Pseudometric-Weighted Correlation Clustering (NeurIPS 2025 · similarity 0.85)
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