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Graph Anomaly Detection on Children

0.704Anomaly F1N2NSC
0.06180.2285250.395250.561975Jun 29, 2026

Evaluation results

MethodLinks
N2NSCLLM Backbone=Qwen3-8B, PEFT=LoRA (rank 64), GNN Encoder=2-layer GAT, GNN heads=8, GNN output dimension=256, Contrastive selection K=22026.060.7041
GINCategory=GNN-based methods2026.060.54317
TAPECategory=Integrating LLMs with graphs2026.060.48365.25
GATCategory=GNN-based methods2026.060.45499.62
GCNCategory=GNN-based methods2026.060.38367.25
XGBGraphCategory=GNN-based methods2026.060.38185
GraphSAGECategory=GNN-based methods2026.060.34295.5
RFGraphCategory=GNN-based methods2026.060.34177.62
PC-GNNCategory=GNN-based methods2026.060.3097.25
GHRNCategory=GNN-based methods2026.060.19949.25
BWGNNCategory=GNN-based methods2026.060.1998
GAAPCategory=GNN-based methods2026.060.161712.25
CoLLCategory=Integrating LLMs with graphs2026.060.155614.62
InstructGLMCategory=Integrating LLMs with graphs2026.060.129112
UNPromptCategory=Integrating LLMs with graphs2026.060.118215.12
PMPCategory=GNN-based methods2026.060.118112.38
ConsisGADCategory=Integrating LLMs with graphs2026.060.099314.12
GraphGPTCategory=Integrating LLMs with graphs2026.060.086517.75