Node Classification On Non Homophilic 10

Métriques

1:1 Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	1:1 Accuracy	Paper Title
NLGAT	29.5 ± 1.3	Non-Local Graph Neural Networks
ACM-SGC-1	35.49 ± 1.06	Revisiting Heterophily For Graph Neural Networks
MixHop	32.22 ± 2.34	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
ACM-GCN++	37.31 ± 1.09	Revisiting Heterophily For Graph Neural Networks
ACM-GCN	36.63 ± 0.84	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN	36.31 ± 1.2	Revisiting Heterophily For Graph Neural Networks
NLMLP	37.9 ± 1.3	Non-Local Graph Neural Networks
H2GCN	35.70 ± 1.00	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Diag-NSD	37.79 ± 1.01	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
GGCN	37.54 ± 1.56	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Geom-GCN	31.59 ± 1.15	Geom-GCN: Geometric Graph Convolutional Networks
ACMII-GCN+	36.14 ± 1.44	Revisiting Heterophily For Graph Neural Networks
GPRGCN	35.16 ± 0.9	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
GloGNN	37.35 ± 1.30	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Gen-NSD	37.80 ± 1.22	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Deformable GCN	37.07±0.79	Deformable Graph Convolutional Networks
GCNII	37.44 ± 1.30	Simple and Deep Graph Convolutional Networks
GloGNN++	37.70 ± 1.40	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
LINKX	36.10 ± 1.55	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
ACM-GCN+	36.26 ± 1.34	Revisiting Heterophily For Graph Neural Networks

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