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ResearchFormerly WizWand

Node Classification on Cora (Accuracy)

88.77AccuracyGCN
55.105263.845172.58581.3249Feb 16, 2024Jul 1, 2024Nov 14, 2024Mar 30, 2025Aug 13, 2025Dec 27, 2025May 13, 2026
Updated 4mo ago

Evaluation results

MethodLinks
GCNSetting=Transductive2024.0588.77
PCNet2025.0988.41
GCNII2025.0988.37
GloGNN++2025.0988.33
GATSetting=Transductive2024.0588.22
BEC-GCN2025.0988.21
WRGAT2025.0988.2
AxelGNN2025.0988.18
PolynormerSetting=Transductive2024.0588.11
GNRF2025.0987.99
GPRGNNSetting=Transductive2024.0587.95
GPRGNN2025.0987.95
GGCN2025.0987.95
ACM-GCN2025.0987.91
NIDGCNSetting=Transductive2024.0587.88
APPNPSetting=Transductive2024.0587.87
H2GCN2025.0987.87
SGFormerSetting=Transductive2024.0587.83
MixHop2025.0987.61
NIDGATSetting=Transductive2024.0587.35
GAT2025.0987.3
Graph-MLPSetting=Transductive2024.0587.06
AxelGNNSim2025.0987.03
GCN2025.0986.98
GraphSAGE2025.0986.9
DirGNN2025.0986.78
VQGraphSetting=Transductive2024.0586.11
LINKX2025.0984.64
GraphMAElearning_mode=unsupervised, dim=5122024.0584.2
GraphACL2025.0984.2
CCA-SSGlearning_mode=unsupervised, dim=5122024.0584
CCA-SSG2025.0984
SDMG2025.0983.6
MVGRLlearning_mode=unsupervised, dim=5122024.0583.5
InfoGCLlearning_mode=unsupervised, dim=5122024.0583.5
SERBackbone=GAT2026.0583.44
CPBackbone=GAT2026.0583.3
P-regBackbone=GCN2026.0583.25
R-regBackbone=GAT2026.0583.17
SP-GCL2025.0983.16
SPGCL2026.0183.1
NASABackbone=GAT2026.0583.07
MVGRL2025.0982.9
VanillaBackbone=GAT2026.0582.9
SERBackbone=GCN2026.0582.85
LapRBackbone=GAT2026.0582.81
PolyGCL2025.0982.74
GMI2025.0982.7
BGRL2025.0982.7
P-regBackbone=GAT2026.0582.45
CPBackbone=GCN2026.0582.44
AFGRL2025.0982.31
DGIlearning_mode=unsupervised, dim=5122024.0582.3
DGI2025.0982.3
R-regBackbone=GCN2026.0582.29
GCN2024.0282.2
EPAGCL2025.0982.14
LapRBackbone=GCN2026.0582.13
ESA2024.0282
VanillaBackbone=GCN2026.0581.54
Pro-GNN2026.0181.3
LOHA2025.0981.22
NASABackbone=GCN2026.0581.03
GraphSAGE2026.0180.9
NIDMAElearning_mode=unsupervised, dim=62024.0580.8
GAT2026.0180.8
DropEdge2026.0180.7
LDS2026.0180.6
GAT2024.0280.3
GCN2026.0180.1
SGC2026.0180
GRACE2025.0980
GIN2024.0278.6
GATv22024.0278.5
GCN-MLP2025.0977.26
HiTeC2025.0874.07
PNA2024.0271.6
GAElearning_mode=unsupervised, dim=162024.0571.5
Geom-GCN2026.0170.8
SCGFMPre-training Domain=Trained on N2 (OOD), Evaluation protocol=5-shot2026.0570.55
SCGFMPre-training Domain=In-Domain, Evaluation protocol=5-shot2026.0570.54
GPS2024.0269.8
S2GAEPre-training Domain=In-Domain, Evaluation protocol=5-shot2026.0566.83
SA2GFMshots=5-shot, attack=targeted evasion attack, p=22025.1166.3
SE-HSSLText Encoder=BERT2025.0864.3
MDGFMshots=5-shot, attack=targeted evasion attack, p=22025.1163.4
SE-HSSLText Encoder=RoBERTa2025.0862.76
TriCLText Encoder=BERT2025.0862.67
GraphCLPre-training Domain=In-Domain, Evaluation protocol=5-shot2026.0560.82
TriCLText Encoder=RoBERTa2025.0860.74
SE-HSSLText Encoder=SE2025.0859.87
BGRLText Encoder=SE2025.0859.72
DropGIN2024.0259.5
GraphCLPre-training Domain=Trained on N2 (OOD), Evaluation protocol=5-shot2026.0559.31
TriCLText Encoder=SE2025.0858.79
MLPlearning_mode=unsupervised, dim=5002024.0557.8
BGRLText Encoder=GIANT2025.0857.44
GraphCLText Encoder=SE2025.0856.69
MDGPTshots=5-shot, attack=targeted evasion attack, p=22025.1156.5
GraphBridgeshots=5-shot, attack=targeted evasion attack, p=22025.1156.4