List your company
ResearchFormerly WizWand

Node Classification on PubMed (standard fixed split)

80.4AccuracyMTGAE
61.47266.38671.376.214Mar 10, 2018Apr 19, 2018May 29, 2018Jul 9, 2018Aug 18, 2018Sep 27, 2018Nov 7, 2018
Updated 8mo ago

Evaluation results

MethodLinks
MTGAEscenario=MTL (Multi-Task Learning)2018.1180.4
AGNNInput=YL, X, G2018.0379.9
N-GCN2018.0979.4
GPNN2018.0979.3
DynamicFilterInput=YL, X, G2018.0379
GCNInput=YL, X, G2018.0379
GCN2018.0979
FeaStNet2018.0979
GAT2018.0979
GCNscenario=task-specific2018.1179
GLNInput=YL, X, G2018.0378.9
SEGCN2018.0978.9
MoNetInput=YL, X, G2018.0378.8
BootstrapInput=YL, X, G2018.0378.8
MoNet2018.0978.8
Bootstrap2018.0978.8
GCN*implementation=own implementation2018.0978.6
PlanetoidInput=YL, X, G2018.0377.2
Planetoid2018.0977.2
Planetoidscenario=task-specific2018.1177.2
node2vecInput=YL, G2018.0375.3
node2vec2018.0975.3
ICAInput=YL, X, G2018.0373.9
DCNNInput=YL, X, G2018.0373
DCNN2018.0973
Multilayer PerceptronInput=YL, XL2018.0371.4
SemiEmbInput=YL, X, G2018.0371.1
ManiRegInput=YL, X, G2018.0370.7
Singlelayer PerceptronInput=YL, XL2018.0369.8
DeepWalkInput=YL, G2018.0365.3
DeepWalk2018.0965.3
LPInput=YL, X, G2018.0363
T-SVMInput=YL, X2018.0362.2