Research map / Learning theory and algorithms / Clustering
Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LP-STC rounding instead of the canonical LP relaxation | Cluster deletion · the evaluated graph datasets | runtime: roughly twice as fast | Helped |
| Use LP-STC rounding instead of the canonical LP relaxation | Cluster deletion · the evaluated graph datasets | lower bound | No clear effect |
| Use match-flip-pivot instead of the canonical LP relaxation | Cluster deletion · the evaluated graph datasets | runtime: 2-3 orders of magnitude faster | Helped |
| Use match-flip-pivot instead of the canonical LP relaxation | Cluster deletion · the evaluated graph datasets | approximation ratio: A posteriori approximation guarantees are always at or below 2.12 | Hurt |
| Use match-flip-pivot instead of LP methods | Cluster editing · caAstroPh and loc-Brightkite | runtime: under a second | Helped |
| Combine MFP-CE lower bounds with standard Pivot on the original graph rather than pivoting on the derived graph | Cluster editing · evaluated graph datasets | approximation ratio | Helped |
| Combine MFP lower bounds with LambdaLouvain | Cluster editing · SNAP and Facebook100 graphs | a posteriori approximation ratio | Helped |
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