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ResearchFormerly WizWand

Node Classification on Texas (60% 20% 20% random splits)

96.72AccuracyP2GNN
66.40474.274582.14590.0155Sep 12, 2021Jun 12, 2022Mar 12, 2023Dec 11, 2023Sep 9, 2024Jun 9, 2025Mar 10, 2026
Updated 7mo ago

Evaluation results

MethodLinks
P2GNNBackbone=ACM-GCN2026.0396.72
ACM-Snowball-22021.0995.74
ACM-GCN2026.0395.57
ACMII-Snowball-22021.0995.25
ACMII-GCN2021.0995.08
ACM-GCN2021.0994.92
VR-GNN2026.0394.86
ACM-Snowball-32021.0994.75
ACMII-Snowball-32021.0994.75
ACM-SGC-12021.0993.61
ACM-SGC-22021.0993.44
ClenshawGCN2022.1093.36
ClenshawGCNSplits=20 random 60%/20%/20% train/validation/test2022.1093.36
ChebNetIISplits=20 random 60%/20%/20% train/validation/test2022.1093.28
ACM-GCNII*2021.0993.28
BernNet2026.0393.12
GPRGNN*2021.0992.92
GPRGNN2026.0392.92
GPRGNNSplits=20 random 60%/20%/20% train/validation/test2022.1092.91
BernNetSplits=20 random 60%/20%/20% train/validation/test2022.1092.62
ACM-GCNII2021.0992.46
MLP-2*2021.0992.26
MLP2026.0392.26
APPNP*2021.0991.18
APPNPSplits=20 random 60%/20%/20% train/validation/test2022.1090.64
GGCN2026.0390.16
FAGCN2021.0988.85
FAGCN2026.0388.85
GCNII*2021.0988.52
MLP2022.1086.81
ChebNetSplits=20 random 60%/20%/20% train/validation/test2022.1086.28
H2GCN2022.1085.9
H2GCN2021.0985.9
H2GCN2026.0385.9
GloGNN++2026.0385.24
ARMASplits=20 random 60%/20%/20% train/validation/test2022.1083.97
SGC-12021.0983.28
GCN2021.0983.11
Snowball-22021.0983.11
Snowball-32021.0983.11
Snowball-22026.0383.11
Snowball-32026.0383.11
GCNII2021.0982.46
GCNII2026.0382.46
SimP-GCN2023.1281.62
SGC-22021.0981.31
SGC-22026.0381.31
GCN+JK2022.1080.66
GCN+JK2021.0980.66
GCN+JK2026.0380.66
GCNII2022.1080.46
GraphSAGE2021.0979.03
GAT*2021.0978.87
GAT2026.0378.87
GCN2022.1076.97
MixHop2022.1076.39
MixHop2021.0976.39
GAT+JK2021.0975.41
GAT+JK2026.0375.41
UGCN2023.1271.72
Geom-GCN2023.1267.57
Geom-GCN†2021.0967.57