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ResearchFormerly WizWand

Graph Classification on IMDB-M (test)

55.28Accuracyatt-Pooling-NLSFs
27.678434.844242.0149.1758Sep 30, 2022Apr 12, 2023Oct 24, 2023May 6, 2024Nov 16, 2024May 30, 2025Dec 11, 2025
Updated 8mo ago

Evaluation results

MethodLinks
att-Pooling-NLSFsArchitecture=Pooling2024.0655.28
ARMA2024.0653.6
LGAN-resresidual=true2025.1253.5
LGANresidual=false2025.1253.3
TopoGCL2025.1252.81
CW Networkscategory=k-WL-based2025.1252.7
ChebNetII2024.0652.69
att-Graph-level NLSFsArchitecture=Graph-level2024.0652.49
GraphTCL2025.1252.4
GINcategory=GNNs2025.1252.3
CayleyNet2024.0651.89
TFGW2025.1250.67
2-WLcategory=Kernels2025.1250.6
PPGNcategory=k-WL-based2025.1250.5
GCN2025.1250.47
DiffPool2024.0650.43
GCN2024.0650.28
delta-2-LWLcategory=k-WL-based2025.1250.2
GIN2025.1250.2
ChebyNet2025.1250.2
APPNP2025.1250.13
GAT2025.1249.93
ChebNet2024.0649.82
GPRGNN2025.1249.8
1-2-3 GNNcategory=k-WL-based2025.1249.5
GPFPre-training Strategy=GCC (MoCo), Tuning Strategy=GPF2022.0949.33
GPFPre-training Strategy=GCC (E2E), Tuning Strategy=GPF2022.0949.17
FTPre-training Strategy=GCC (E2E), Tuning Strategy=FT2022.0949.07
OT-GNN2025.1249
WL-Kernel2025.1248.53
FTPre-training Strategy=GCC (MoCo), Tuning Strategy=FT2022.0948.07
DGCNNcategory=GNNs2025.1247.8
GPRGNN2024.0647.07
SAGE2024.0646.94
WL2024.0646.63
GAT2024.0645.67
PATCHY-SANcategory=GNNs2025.1245.2
APPNP2024.0644.36
GK2024.0644.19
PGOT12025.1243.07
DIFFPOOL2025.1242.08
SPcategory=Kernels2025.1240.5
DropGIN2025.1237.33
DCNNcategory=GNNs2025.1233.5
SP-Kernel2025.1228.74