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Research map / Graph learning / Graph self-supervised learning

Hypergraph p-Laplacian: A Differential Geometry View

AAAI 2018 · Graph learning › Graph self-supervised learning · approach: Heterophily and imbalance on graphs · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Tune p instead of fixing it to 2 (ablation)Proposed hypergraph p-Laplacian · Mushroom two-class clusteringerror rate: 0.3156 -> 0.2329Helped
Tune p instead of fixing it to 2 (ablation)Proposed hypergraph p-Laplacian · Breast Cancer two-class clusteringerror rate: 0.0286 -> 0.0243Helped
Tune p instead of fixing it to 2 (ablation)Proposed hypergraph p-Laplacian · Chess two-class clusteringerror rate: 0.4775 -> 0.2847Helped
Tune p instead of fixing it to 2 (ablation)Proposed hypergraph p-Laplacian · Congress two-class clusteringerror rate: 0.1241 -> 0.1195Helped
Use the tuned p-Laplacian rather than Zhou's LaplacianHypergraph clustering · Mushroom two-class clusteringerror rate: 0.3156 -> 0.2329Helped
Use the tuned p-Laplacian rather than Zhou's LaplacianHypergraph clustering · Breast Cancer two-class clusteringerror rate: 0.0300 -> 0.0243Helped
Use the tuned p-Laplacian rather than Zhou's LaplacianHypergraph clustering · Chess two-class clusteringerror rate: 0.4925 -> 0.2847Helped
Use the tuned p-Laplacian rather than Zhou's LaplacianHypergraph clustering · Congress two-class clusteringerror rate: 0.1241 -> 0.1195Helped
Use the tuned p-Laplacian rather than Hein's regularizerHypergraph clustering · Mushroom two-class clusteringerror rate: 0.1349 -> 0.2329Hurt
Use the tuned p-Laplacian rather than Hein's regularizerHypergraph clustering · Breast Cancer two-class clusteringerror rate: 0.3362 -> 0.0243Helped
Use the tuned p-Laplacian rather than Hein's regularizerHypergraph clustering · Chess two-class clusteringerror rate: 0.4778 -> 0.2847Helped
Use the tuned p-Laplacian rather than Hein's regularizerHypergraph clustering · Congress two-class clusteringerror rate: 0.3034 -> 0.1195Helped

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