Research map / Graph learning / Graph algorithms and clustering
KerGM: Kernelized Graph Matching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use KerGM with approximate explicit feature maps (KerGM_II) | Lawler graph matching · Synthetic Erdos–Rényi graphs | matching accuracy | Helped |
| Use KerGM_II instead of the baseline aligners | Lawler graph matching · Synthetic fully connected graphs, up to 2000 nodes | scalability | Helped |
| Use KerGM_I | Graph matching · CMU House Sequence | matching accuracy | Helped |
| Use KerGM_I rather than FGM | Graph matching with outliers · CMU House Sequence, (n1,n2)=(20,30) | matching accuracy | Helped |
| Use KerGM_I, BPFG, or FGM rather than other methods | Graph matching · Pascal | matching accuracy | Helped |
| Use KerGM_I or BPFG rather than FGM | Graph matching with increasing outliers · Pascal | matching accuracy | Helped |
| Use KerGM_II rather than TAME, GHOST, or MAGNA | PPI network alignment · S.cerevisiae (yeast) PPI network | matching accuracy | Helped |
| Reduce the entropy regularization parameter λ (ablation) | KerGM_II · Large synthetic subgraph matching | matching accuracy | Helped |
| Vary the Fourier random feature dimension D (ablation) | KerGM_II · Large synthetic subgraph matching | matching accuracy | No clear effect |
| Replace the Hungarian direction solver with the Sinkhorn-Knopp solver in EnFW | Frank-Wolfe optimization for graph matching · Graph matching optimization | runtime | Helped |
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