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ResearchFormerly WizWand

Graph Classification on IMDB MULTI

64.2Accuracyδ-3-LWL+
46.655251.210155.76560.3199May 21, 2018Sep 18, 2019Jan 16, 2021May 17, 2022Sep 15, 2023Jan 13, 2025May 14, 2026
Updated 4mo ago

Evaluation results

MethodLinks
δ-3-LWL+Type=Local Kernel2019.0464.2
δ-2-LWL+Type=Local Kernel2019.0462.5
SGFormerMethod Category=Graph Transformers2025.0856.4
ISP-GNN†Evaluation protocol=10-fold cross-validation2026.0356.3
ISP-GNN_DegreeEvaluation protocol=10-fold cross-validation2026.0355.9
ISP-GNN_CoreEvaluation protocol=10-fold cross-validation2026.0355.7
ISP-GNN_OnionEvaluation protocol=10-fold cross-validation2026.0355.6
KP-GINEvaluation protocol=10-fold cross-validation2026.0355.5
ID-GNNEvaluation protocol=10-fold cross-validation2026.0355.4
GraphSNNEvaluation protocol=10-fold cross-validation2026.0355.3
GIN-AK+Evaluation protocol=10-fold cross-validation2026.0355.2
GCNMethod Category=GNN methods2025.0855.2
AC-GINEvaluation protocol=10-fold cross-validation2026.0355.1
NC-GNNEvaluation protocol=10-fold cross-validation2026.0355
UnionGINEvaluation protocol=10-fold cross-validation2026.0355
BEC-GINEvaluation protocol=10-fold cross-validation2026.0354.9
GINEvaluation protocol=10-fold cross-validation2026.0354.6
GSNEvaluation protocol=10-fold cross-validation2026.0354.6
DropGINEvaluation protocol=10-fold cross-validation2026.0354.3
GIN2019.0452.3
GIN2018.1152.3
Cluster-GT2024.1052.13
CORE-WL-VH2024.0651.7
CORE-WL-VHMethod Category=Kernels2025.1051.7
GIC2018.1151.66
GEFRFE-learnable(α)Method Category=GEFRFE (GEFRFE-learnable(α))2025.0851.6
SEP-G2024.1051.53
1-WLMethod category=Kernel2018.1051.5
DiffPool2024.1051.31
WL-VH2024.0651.3
WL-VHMethod Category=Kernels2025.1051.3
WL-OA2024.0651.1
WL-OAMethod Category=Kernels2025.1051.1
MinCutPool2024.1051.04
CORE-SP2024.0651
WL subtree2026.0551
CORE-SPMethod Category=Kernels2025.1051
WL kernelMethod Category=Kernel methods2025.0850.9
Baseline2025.1050.82
DiffusionMethod Category=Diffusion Baselines2025.1050.8
ASAP2024.1050.78
NGRAMCNN2018.1150.66
GMT2024.1050.66
2-WLMethod category=Kernel2018.1050.6
NH2024.0650.5
NHMethod Category=Kernels2025.1050.5
WL-OAMethod category=Kernel2018.1050.4
GCN2024.1050.39
StructPool2024.1050.23
SAGPool(H)2024.1050.23
δ-2-LWLType=Local Kernel2019.0450.2
Set2Set2024.1050.19
WLOAType=Baseline2019.0450.1
1-WLType=Baseline2019.0450
LDP2018.1150
LDPClassifier=SVM2018.1150
Gin-0Type=Neural2019.0449.9
WL2019.0449.8
WL2018.1149.8
GRF++2025.1049.8
3-WLMethod category=Kernel2018.1049.7
Gin-εType=Neural2019.0449.6
WKPI-KC2019.0449.5
1-2-3-GNNTuning status=Tuned, Method category=GNN2018.1049.5
SAGPool(G)2024.1049.47
δ-3-LWLType=Local Kernel2019.0449.3
SAEN2018.1149.11
WL-PM2024.0649.1
WL-PMMethod Category=Kernels2025.1049.1
GRF++Method Category=Diffusion Baselines, degree=22025.1049.1
GraphormerMethod Category=Graph Transformers2025.0848.9
1-2-3-GNNTuning status=No Tuning, Method category=GNN2018.1048.8
GRFMethod Category=Diffusion Baselines2025.1048.7
PF2019.0448.6
TopKPool2024.1048.59
Space OptimumRefinement budget=1002025.0748.56
GCAPS-CNNcategory=Deep Learning Method2018.0548.5
GINMethod Category=GNNs2025.1048.5
GRF2025.1048.33
SortPool2024.1048.18
GIN2024.1048.13
DYF2018.1148.12
2-WLType=Global Kernel2019.0448.1
δ-2-WLType=Global Kernel2019.0447.9
DGCNNcategory=Deep Learning Method2018.0547.83
DGCNNMethod category=GNN2018.1047.8
DGCNN2018.1147.8
DGCNNMethod Category=GNN methods2025.0847.8
RetGK2019.0447.7
1-GNNTuning status=Tuned, Method category=GNN2018.1047.7
RetGk2018.1147.7
ML2024.0647.7
MLMethod Category=Kernels2025.1047.7
RLRefinement budget=1002025.0747.65
M-DESIGNRefinement budget=1002025.0747.65
BGFI2024.0647.6
GraphSAGEMethod Category=GNNs2025.1047.6
DesiGNNRefinement budget=1002025.0747.44
FB2018.1147.34
RandomRefinement budget=1002025.0747.33