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ResearchFormerly WizWand

Graph Classification on ogbg-molpcba (test)

32.43APGPTrans-L
27.614828.864930.11531.3651Oct 19, 2020Sep 18, 2021Aug 19, 2022Jul 20, 2023Jun 18, 2024May 19, 2025Apr 19, 2026
Updated 4mo ago

Evaluation results

MethodLinks
GPTrans-LParams=86.0M, Pre-trained on PCQM4M-v2=true2023.1232.43
GraphGPT-B (24 layers)Params=227.3M, Pre-trained on PCQM4M-v2=true, Layers=242023.1231.81
GRPE-LargeParams=118.3M, Pre-trained on PCQM4M-v2=true2023.1231.5
GatedGCNvirtual node=true2024.0631.41
Graphormer-FLAG#Param=119.5M, Pre-training=true2021.0831.4
Graphormer-LParams=119.5M, Pre-trained on PCQM4M-v2=true2023.1231.4
GatedGCNhierarchical support graph=true2024.0631.29
GraphGPT-B (12 layers)Params=113.6M, Pre-trained on PCQM4M-v2=true, Layers=122023.1231.28
NeuralWalker2024.0630.86
GatedGCN2024.0630.66
PDFNumber of parameters=3.8m2023.0530.31
PDIFFParams=3.84M, Pre-trained on PCQM4M-v2=false2023.1230.31
GraphGPT-MParams=37.7M, Pre-trained on PCQM4M-v2=true2023.1230.13
Nested GINVirtual node=true, Ensemble=true2021.1030.07
NGIN-VNParams=44.19M, Pre-trained on PCQM4M-v2=false2023.1230.07
CRaWl2022.0529.86
CRAWLparameter budget=500k2021.0229.86
CRAWL2024.0629.86
SigGate-GT2026.0429.84
GatedGCN+enhanced=true2025.0229.81
GINE-APPNPNumber of Parameters=6.15m2021.1229.79
GINE-APNumber of parameters=6.2m2023.0529.79
Specformer2023.1029.72
Specformer2025.0229.72
Spec-GNNumber of Parameters=1.74m2021.1229.65
EGT (30 layers)#Param=110.8M, Pre-training=true2021.0829.61
EGTpre-trained=true, additional pretraining datasets=true2025.0229.61
GECO2025.0229.61
EGT-LargerParams=110.8M, Pre-trained on PCQM4M-v2=true2023.1229.61
Norm-GNNumber of Parameters=1.74m2021.1229.51
PHC-GNNVirtual node=false2021.1029.47
PHC-GNN2021.1029.47
PHC-GNN#Param=1.69M2021.0829.47
PHC-GNNNumber of Parameters=1.69m2021.1229.47
PHC-GNNumber of parameters=1.7m2023.0529.47
Hodge1Lapbackbone=GPS2023.1029.37
EdgeRWSEbackbone=GPS2023.1029.34
GIN-AK+2022.0529.3
GIN-AK+2021.0229.3
GIN-AK+2023.1029.3
GNNAK+Number of runs=42023.0929.3
Exphormer2026.0429.2
GINEVirtual node=true2021.1029.17
GINE-VN#Param=6.15M2021.0829.17
GINE-vnNumber of Parameters=6.15m2021.1229.17
GINE+2021.0229.17
GINE-VNParams=6.1M, Pre-trained on PCQM4M-v2=false2023.1229.17
TransformerComplexity=O(N^2), Positional Encoding=Baseline2025.0929.1
GPS2022.0529.07
GPSNumber of parameters=9.7m2023.0529.07
GPS2023.1029.07
GPS2024.0629.07
GPS2024.0629.07
GraphGPS2025.0229.07
GraphGPS2026.0429.07
GIN-VN#Param=3.4M, Pre-training=true2021.0829.02
DGN2021.1028.85
DGN#Param=6.73M2021.0828.85
DGNNumber of Parameters=6.73m2021.1228.85
DGN2022.0528.85
DGNNumber of parameters=6.7m2023.0528.85
DGNNumber of runs=42023.0928.85
GDeRRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Dynamic2024.1028.5
Exphormer2025.0228.49
DeeperGCN + FLAGBackbone=DeeperGCN, Virtual Nodes=false2020.1028.42
DeeperGCN-VN-FLAG#Param=6.55M2021.0828.42
DeeperGNumber of Parameters=5.55m2021.1228.42
DeeperGNumber of parameters=5.6m2023.0528.42
PNA-LSPE2021.1028.4
PNA2021.1028.38
PNA#Param=6.55M2021.0828.38
PNANumber of Parameters=6.55m2021.1228.38
PNA2022.0528.38
PNANumber of parameters=6.6m2023.0528.38
PNA2021.0228.38
PNA2023.1028.38
PNANumber of runs=42023.0928.38
PNA2026.0428.38
GIN-Virtual + FLAGBackbone=GIN, Virtual Nodes=true2020.1028.34
Nested GINVirtual node=true2021.1028.32
Nested GIN2021.0228.32
Nested GINNumber of runs=4, Virtual node=true2023.0928.32
MarginRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1028.3
InfoBatchRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Dynamic2024.1028.3
UCBRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Dynamic2024.1028.1
Whole DatasetRemaining Ratio %=100%, Backbone=PNA2024.1028.1
Hard RandomRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1028
GraNd-4Remaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1028
GDeRRemaining Ratio %=50%, Backbone=PNA, Pruning Type=Dynamic2024.1028
ForgettingRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1027.9
GlisterRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1027.9
DeeperGCNvirtual_node=true2021.1027.81
DeeperGCNBackbone=DeeperGCN, Virtual Nodes=false2020.1027.81
DeeperGCN2022.0527.81
DeeperGCN2026.0427.81
CDRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1027.8
Least ConfidenceRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1027.8
GraNd-20Remaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1027.8
CraigRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Static2024.1027.8
Soft RandomRemaining Ratio %=70%, Backbone=PNA, Pruning Type=Dynamic2024.1027.8