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ResearchFormerly WizWand

Graph Classification on COLLAB

88AccuracyGraphMAE
79.88881.99484.186.206Jun 3, 2016Feb 6, 2018Oct 13, 2019Jun 17, 2021Feb 21, 2023Oct 26, 2024Jul 2, 2026
Updated 4mo ago

Evaluation results

MethodLinks
GraphMAEEvaluation protocol=Unsupervised representation learning2024.0588
AD-GCLEvaluation protocol=Unsupervised representation learning2024.0585.5
MVGRLEvaluation protocol=Unsupervised representation learning2024.0584.5
CORE-WL-VH2024.0684.5
CORE-WL-VHMethod Category=Kernels2025.1084.5
GraphMAE2 + COREmasking process=GraphMAE22025.1284.3
GraphMAE22025.1284.1
PXGL-GNN2025.1283.96
DiGGRLearning Paradigm=Unsupervised2024.0883.76
GNTKinput=constant (X=1)2019.0583.6
GraphMAE + COREmasking process=GraphMAE2025.1283.4
AIA2022.1182.98
M-Mixup2022.1182.9
GCBM-E2026.0382.8
SAN2025.1282.73
GraphMAEApproach Category=Feature-based2024.1082.7
CAL2022.1182.68
FLAG2022.1182.48
GraphCrocEpochs=1002024.1082.4
GCBM2026.0382.4
CDLLabel=70%2024.1182.36
S2GAE2025.1282.35
lrGAE 6Approach Category=Structure-based2024.1082.3
lrGAE 7Approach Category=Structure-based2024.1082.3
S2GAEApproach Category=Structure-based2024.1082.2
lrGAE 8Approach Category=Structure-based2024.1082.2
DiffPool-DETlearning_type=Inductive learning2019.0582.13
ERM2022.1182.08
MaskGAEApproach Category=Structure-based2024.1082
Graphormer2026.0781.8
GCNnode features=augmented with degree, input=constant (X=1)2019.0581.72
DDMProtocol=Unsupervised representation learning2023.0681.72
GraphCrocEpochs=102024.1081.7
GraphMAE2Approach Category=Feature-based2024.1081.7
GFNK=3, node features=degree + multi-scale, input=constant (X=1)2019.0581.5
GFNInference Setting=Inductive, Evaluation Protocol=10-fold cross-validation2019.0981.5
GAWL2026.0381.5
GFN# Parameters=68,7542023.1181.5
GFN2023.1181.5
DisenSemiLearning Paradigm=Supervised/Semi-supervised2024.0881.5
BGRLApproach Category=Standard GCL2024.1081.5
ICL2025.1281.45
WWL2026.0381.4
GraphGPS2026.0781.4
PPGNInference Setting=Inductive, Evaluation Protocol=10-fold cross-validation2019.0981.38
GFN-lightK=3, node features=degree + multi-scale, input=constant (X=1)2019.0581.34
GCMAE2025.1281.3
SEP2026.0381.3
SEP-G2024.1081.28
SubGNN2025.1281.26
GIC2018.1181.24
DGCLLearning Paradigm=Unsupervised2024.0881.2
GLIALabel=70%2024.1181.18
NH2024.0681.1
NHMethod Category=Kernels2025.1081.1
S2GAE2024.1081.02
RetGK_Iinput=constant (X=1)2019.0581
RetGk2018.1181
CDLLabel=50%2024.1180.96
CaCoSE2026.0380.95
MINCUTPOOL2026.0380.9
(s)GCNneighborhood enlargement=true2019.0580.89
MinCutPool2024.1080.87
GCLLabel=70%2024.1180.82
SeeGeraApproach Category=Feature-based2024.1080.8
Trans. with X-LogSMaskvariant=ours2026.0780.8
GMT2024.1080.74
GMT2026.0780.74
(s)GIN-0neighborhood enlargement=true, epsilon=02019.0580.71
WL-OAKernel Type=Weisfeiler-Lehman Optimal Assignment2016.0680.7
WL-OA2018.1180.7
OT-GNN2026.0380.7
GMT2026.0380.7
S3GCLApproach Category=Standard GCL2024.1080.7
GatedGCN2026.0780.7
DYF2018.1180.61
GCNPooling Strategy=Flat2019.0380.6
RetGK_IIinput=constant (X=1)2019.0580.6
SAT2026.0380.6
DropEdge2022.1180.59
GCN2024.1080.59
StructMAE2024.1080.53
(s)GIN-epsilonneighborhood enlargement=true2019.0580.51
WL-OA2024.0680.5
AUG-MAE2025.1280.5
WL-OAMethod Category=Kernels2025.1080.5
Cluster-GT2024.1080.43
HGLET + SVMInput Type=Combo, # Parameters=2562023.1180.39
HGLET + SVMFeature Type=Combo2023.1180.39
GraphMAEProtocol=Unsupervised representation learning2023.0680.32
GraphMAE2024.1080.32
GraphMAELearning Paradigm=Unsupervised2024.0880.32
GraphMAE2025.1280.3
GRAPHORMER2026.0380.3
GIN-0neighborhood enlargement=false, epsilon=02019.0580.2
GINinput=constant (X=1)2019.0580.2
GIN-0Learning paradigm=Supervised Approaches2021.1080.2
GIN-0Inference Setting=Inductive, Evaluation Protocol=10-fold cross-validation2019.0980.2
GIN2018.1180.2
GINProtocol=Unsupervised representation learning2023.0680.2