Research map / Graph learning / Graph algorithms and clustering
Gromov-Wasserstein Factorization Models for Graph Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GWF-PPA instead of the strongest baseline | Graph clustering · AIDS | clustering accuracy: 91.0±0.7 -> 99.5±0.4 | Helped |
| Use GWF-PPA instead of the strongest baseline | Graph clustering · PROTEIN | clustering accuracy: 66.4±0.8 -> 70.7±0.7 | Helped |
| Use GWF-PPA instead of the strongest baseline | Graph clustering · PROTEIN-F | clustering accuracy: 66.0±0.9 -> 69.3±0.8 | Helped |
| Use GWF-PPA instead of the strongest baseline | Graph clustering · IMDB-B | clustering accuracy: 56.7±1.5 -> 60.2±1.6 | Helped |
| Use PPA rather than BADMM in the GWD module (ablation) | GWF graph clustering · AIDS | clustering accuracy: 97.6±0.8 -> 99.5±0.4 | Helped |
| Use PPA rather than BADMM in the GWD module (ablation) | GWF graph clustering · PROTEIN | clustering accuracy: 69.2±1.0 -> 70.7±0.7 | Helped |
| Use PPA rather than BADMM in the GWD module (ablation) | GWF graph clustering · PROTEIN-F | clustering accuracy: 68.1±1.1 -> 69.3±0.8 | Helped |
| Use PPA rather than BADMM in the GWD module (ablation) | GWF graph clustering · IMDB-B | clustering accuracy: 55.9±1.8 -> 60.2±1.6 | Helped |
| Use GWF-PPA instead of FGWK | Graph classification · PROTEIN | classification accuracy: 75.1±2.9 -> 73.7±2.0 | Hurt |
| Use GWF-PPA instead of FGWK | Graph classification · IMDB-B | classification accuracy: 64.2±3.3 -> 63.9±2.7 | Hurt |
Closest papers by meaning
- Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching (NeurIPS 2019 · similarity 0.92)
- THESAURUS: Contrastive Graph Clustering by Swapping Fused Gromov-Wasserstein Couplings (AAAI 2025 · similarity 0.89)
- Semi-relaxed Gromov-Wasserstein divergence and applications on graphs (ICLR 2022 · similarity 0.89)
- Online Sparsification of Bipartite-Like Clusters in Graphs (ICML 2025 · similarity 0.87)
- Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability (ICML 2025 · similarity 0.86)
- A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering (ICML 2024 · similarity 0.86)
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