TERGAD augments graph anomaly detection by converting node topological properties into LLM-generated semantic embeddings that are fused with original attributes via a gated dual-branch autoencoder for joint reconstruction-based anomaly scoring.
Federated graph neural network for fast anomaly detection in controller area networks,
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TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
TERGAD augments graph anomaly detection by converting node topological properties into LLM-generated semantic embeddings that are fused with original attributes via a gated dual-branch autoencoder for joint reconstruction-based anomaly scoring.