List your company
ResearchFormerly WizWand

Node Classification on Pubmed standard (test)

85.2AccuracyDFNet-ATT
20.82437.53754.2570.963Sep 25, 2019May 28, 2020Jan 29, 2021Oct 2, 2021Jun 5, 2022Feb 6, 2023Oct 11, 2023
Updated 8mo ago

Evaluation results

MethodLinks
DFNet-ATT2019.1085.2
DFNet2019.1084.3
DF-ATT2019.1082.3
GATarchitecture=DenseBlock2019.1081.8
DDMLearning Setup=unsupervised representation learning with linear classifier2023.0681.7
GCNarchitecture=DenseBlock2019.1081.5
GraphMix (GAT)Source=Our Experiments, Training Strategy=GraphMix, Backbone=GAT2019.0981.1
GraphMAELearning Setup=unsupervised representation learning with linear classifier2023.0681.1
CCA-SSGLearning Setup=unsupervised representation learning with linear classifier2023.0681
GraphMix (GCN)Source=Our Experiments, Training Strategy=GraphMix, Backbone=GCN2019.0980.98
DisenGCNSource=Reported in literature2019.0980.5
GCN (self-training)Source=Our Experiments, Training Strategy=Self-training2019.0980.42
MVGRLLearning Setup=unsupervised representation learning with linear classifier2023.0680.1
BVATSource=Reported in literature2019.0979.9
GCNSource=Our Experiments, Training Strategy=Supervised2019.0979.86
GAT (self-training)Source=Our Experiments, Training Strategy=Self-training2019.0979.67
Graph U-NetSource=Reported in literature2019.0979.6
BGRLLearning Setup=unsupervised representation learning with linear classifier2023.0679.6
InfoGCLLearning Setup=unsupervised representation learning with linear classifier2023.0679.1
GATSource=Our Experiments, Training Strategy=Supervised2019.0979.05
GCN2019.1079
GAT2019.1079
GCNSource=Reported in literature2019.0979
GATSource=Reported in literature2019.0979
GATLearning Setup=unsupervised representation learning with linear classifier2023.0679
ARMA2019.1078.9
MoNetSource=Reported in literature2019.0978.8
GraphMix (Graph U-Net)Source=Our Experiments, Training Strategy=GraphMix, Backbone=Graph U-Net2019.0978.76
LNet2019.1078.3
Graph-U-Net (self-training)Source=Our Experiments, Training Strategy=Self-training2019.0978.12
AdaLNet2019.1078.1
Graph U-NetSource=Our Experiments, Training Strategy=Supervised2019.0977.73
Balanced LL-orthogonal initializationLayers=402023.1077.5
FCN (self-training)Source=Our Experiments, Training Strategy=Self-training2019.0977.4
Balanced LL-orthogonal initializationLayers=202023.1077.3
Planetoid2019.1077.2
PlanetoidSource=Reported in literature2019.0977.2
Balanced LL-orthogonal initializationLayers=102023.1076.9
DGILearning Setup=unsupervised representation learning with linear classifier2023.0676.8
GPT-GNNLearning Setup=unsupervised representation learning with linear classifier2023.0676.3
Balanced LL-orthogonal initializationLayers=22023.1076
Balanced LL-orthogonal initializationLayers=52023.1075.7
Lipschitz normalizationLayers=2, Base Initialization=Xavier2023.1074.8
Chebyshev2019.1074.4
ChebyshevSource=Reported in literature2019.0974.4
ICA2019.1073.9
ICASource=Reported in literature2019.0973.9
Lipschitz normalizationLayers=5, Base Initialization=Xavier2023.1073.7
MT-GCNLabeled per class=7, Label rate=0.1%2020.0973.1
Co-trainingLabeled per class=7, Label rate=0.1%2020.0972.7
SemiEmbSource=Reported in literature2019.0971.7
MLPSource=Reported in literature2019.0971.4
SemiEmb2019.1071.1
UnionLabeled per class=7, Label rate=0.1%2020.0970.7
ManiRegSource=Reported in literature2019.0970.7
M3SLabeled per class=7, Label rate=0.1%2020.0970.6
MultiStageLabeled per class=7, Label rate=0.1%2020.0970.2
MT-GCNLabeled per class=3, Label rate=0.05%2020.0969.5
IntersectionLabeled per class=7, Label rate=0.1%2020.0969.4
Co-trainingLabeled per class=3, Label rate=0.05%2020.0968.3
Self-trainingLabeled per class=7, Label rate=0.1%2020.0966.8
LPLabeled per class=3, Label rate=0.05%2020.0966.4
GCN+VLabeled per class=7, Label rate=0.1%2020.0965.9
MT-GCNLabeled per class=2, Label rate=0.03%2020.0965.5
LPLabeled per class=7, Label rate=0.1%2020.0965.4
DeepWalk2019.1065.3
DeepWalkSource=Reported in literature2019.0965.3
M3SLabeled per class=3, Label rate=0.05%2020.0964.4
MultiStageLabeled per class=3, Label rate=0.05%2020.0964.3
UnionLabeled per class=3, Label rate=0.05%2020.0964
LP2019.1063
LPSource=Reported in literature2019.0963
Co-trainingLabeled per class=2, Label rate=0.03%2020.0962.2
LPLabeled per class=2, Label rate=0.03%2020.0961.4
GCN+VLabeled per class=2, Label rate=0.03%2020.0960.5
IntersectionLabeled per class=3, Label rate=0.05%2020.0959.3
M3SLabeled per class=2, Label rate=0.03%2020.0959.2
Self-trainingLabeled per class=3, Label rate=0.05%2020.0958.7
UnionLabeled per class=2, Label rate=0.03%2020.0958.4
GCN+VLabeled per class=3, Label rate=0.05%2020.0957.5
MultiStageLabeled per class=2, Label rate=0.03%2020.0957.4
GCN-VLabeled per class=7, Label rate=0.1%2020.0956.3
Lipschitz normalizationLayers=10, Base Initialization=Xavier2023.1052.8
IntersectionLabeled per class=2, Label rate=0.03%2020.0952
Self-trainingLabeled per class=2, Label rate=0.03%2020.0951.9
ChebyshevLabeled per class=7, Label rate=0.1%2020.0951.2
GCN-VLabeled per class=3, Label rate=0.05%2020.0949.7
ChebyshevLabeled per class=3, Label rate=0.05%2020.0947.3
GCN-VLabeled per class=2, Label rate=0.03%2020.0946.4
ChebyshevLabeled per class=2, Label rate=0.03%2020.0940.4
Lipschitz normalizationLayers=40, Base Initialization=Xavier2023.1036.6
Lipschitz normalizationLayers=20, Base Initialization=Xavier2023.1023.3