Research map / Learning theory and algorithms / Clustering
Modification-Fair Cluster Editing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Require perfect modification fairness instead of standard Cluster Editing | Cluster Editing on Facebook subgraphs · Facebook subgraphs, Set 1 | edit count ratio to non-fair optimum: 3.48 | Hurt |
| Require perfect modification fairness instead of standard Cluster Editing | Cluster Editing on Facebook subgraphs · Facebook subgraphs, Set 2 | edit count ratio to non-fair optimum: 1.31 | Hurt |
| Require perfect modification fairness instead of standard Cluster Editing | Cluster Editing on Facebook subgraphs · Facebook subgraphs, Set 3 | edit count ratio to non-fair optimum: 1.31 | Hurt |
| Require perfect modification fairness instead of standard Cluster Editing | Cluster Editing on Facebook subgraphs · Facebook subgraphs, Set 4 | edit count ratio to non-fair optimum: 1.16 | Hurt |
| Allow normalized fairness slack δnorm = 0.02 rather than require perfect fairness | Modification-Fair Cluster Editing · Facebook subgraphs, Set 1 | edit count ratio to non-fair optimum: 3.48 -> 3.31 | Helped |
| Allow normalized fairness slack δnorm = 0.02 rather than require perfect fairness | Modification-Fair Cluster Editing · Facebook subgraphs, Set 2 | edit count ratio to non-fair optimum: 1.31 -> 1.08 | Helped |
| Allow normalized fairness slack δnorm = 0.02 rather than require perfect fairness | Modification-Fair Cluster Editing · Facebook subgraphs, Set 3 | edit count ratio to non-fair optimum: 1.31 -> 1.02 | Helped |
| Allow normalized fairness slack δnorm = 0.02 rather than require perfect fairness | Modification-Fair Cluster Editing · Facebook subgraphs, Set 4 | edit count ratio to non-fair optimum: 1.16 -> 1.03 | Helped |
| Impose modification fairness on the standard solution | Cluster Editing on Facebook subgraphs · Sets 2, 3, and 4 | edit count increase: at most 33%, 10%, and 8% | Mixed |
| Allow fairness slack rather than demand perfect fairness | Modification-Fair Cluster Editing · Facebook subgraphs | running time: 100 times | Helped |
Closest papers by meaning
- Accelerating Spectral Clustering under Fairness Constraints (ICML 2025 · similarity 0.86)
- Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges (ICML 2025 · similarity 0.86)
- Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better (ICML 2024 · similarity 0.86)
- FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning (AAAI 2025 · similarity 0.85)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.85)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.85)
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