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Graph Anomaly Detection on DGraph

0.708AUROCSAGAD
0.42460.4981750.571750.645325Feb 19, 2024Jun 29, 2024Nov 7, 2024Mar 19, 2025Jul 28, 2025Dec 6, 2025Apr 17, 2026

Evaluation results

MethodLinks
SAGAD2026.030.708—
GAS2026.030.699—
H2GCN2026.030.69—
BernNet2026.030.69—
PCGNN2026.030.684—
ACM2026.030.676—
AdaGNN2026.030.676—
GAT2026.030.672—
AMNet2026.030.671—
GHRN2026.030.671—
ConsisGAD2026.030.671—
GCN2026.030.662—
GIN2026.030.657—
BWGNN2026.030.655—
DCI2026.030.653—
SAGE2026.030.648—
SpaceGNN2026.030.639—
FAGCN2026.030.63—
XGBGraph2026.030.624—
GGADSetting=Semi-supervised, Training normal nodes=15%2024.020.59430.0082
OCGNNSetting=Semi-supervised, Training normal nodes=15%2024.020.5866—
DOMINANTSetting=Semi-supervised, Training normal nodes=15%2024.020.58510.0076
AnomalyDAESetting=Unsupervised2024.020.57630.007
DOMINANTSetting=Unsupervised2024.020.57380.0075
NK-GAD2026.040.553—
SmoothGNN2026.040.5044—
ADA-GAD2026.040.4899—
GAD-NR2026.040.4763—
GADAM2026.040.4522—
AEGISSetting=Unsupervised2024.020.45090.0053
AnomalyDAESetting=Semi-supervised, Training normal nodes=15%2024.020.43550.0071
AEGISSetting=Semi-supervised, Training normal nodes=15%2024.02—0.0058