Research map / Learning theory and algorithms / Clustering
Online and Consistent Correlation Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use AGREE-ON (lazy, origin-cluster-based online agreement) instead of PIVOT for online correlation clustering | online correlation clustering · four SNAP graphs (email-Enron, ca-AstroPh, musae-facebook, cit-HepTh) | maximum recourse per node: e.g. 14 -> 1 on email-Enron | Helped |
| Use AGREE-ON instead of AGREE-OFF (rerun Agreement each arrival with no consistency rules) | online correlation clustering · four SNAP graphs | maximum recourse per node: e.g. 5 -> 1 on cit-HepTh | Helped |
| Use AGREE-ON instead of AGREE-OFF | online correlation clustering · cit-HepTh and other datasets | total recourse: up to an order of magnitude less | Helped |
| Use AGREE-ON instead of PIVOT | online correlation clustering · cit-HepTh | total recourse: 1-2 orders of magnitude less than PIVOT | Helped |
| Use AGREE-ON instead of AGREE-OFF | online correlation clustering · four SNAP graphs | clustering cost: slightly better in all datasets | Helped |
| Use AGREE-ON instead of PIVOT | online correlation clustering · all datasets except ca-AstroPh | clustering cost | Helped |
| Use AGREE-ON instead of PIVOT on ca-AstroPh | online correlation clustering · ca-AstroPh | clustering cost | No clear effect |
| Use AGREE-ON instead of PIVOT in the online setting with recourse, sparse graphs | online correlation clustering · cit-HepTh | clustering cost relative to SINGLETONS | Helped |
Closest papers by meaning
- Efficient Testing for Correlation Clustering: Improved Algorithms and Optimal Bounds (ICLR 2026 · similarity 0.91)
- Improved Approximation Algorithms for Chromatic and Pseudometric-Weighted Correlation Clustering (NeurIPS 2025 · similarity 0.88)
- Learning-Augmented Streaming Algorithms for Correlation Clustering (NeurIPS 2025 · similarity 0.88)
- Towards Better-than-2 Approximation for Constrained Correlation Clustering (ICML 2025 · similarity 0.87)
- Query-Efficient Correlation Clustering with Noisy Oracle (NeurIPS 2024 · similarity 0.87)
- Dynamic Correlation Clustering in Sublinear Update Time (ICML 2024 · similarity 0.87)
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