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Proposes a node-level spectral energy formulation for detecting camouflaged anomalies in graphs, extending to spatio-temporal settings with energy-driven message passing. Demonstrates effectiveness on large-scale benchmarks.
Proposes DDGAD, a diffusion-based framework for graph anomaly detection that uses trajectory dynamics to distinguish normal from anomalous nodes, mitigating contamination propagation via a reliability-aware consensus mechanism and three complementary anomaly signals.
TERGAD is a novel data augmentation framework that uses large language models to translate node-level topological properties into semantic narratives, then fuses these with original node attributes via a gated dual-branch autoencoder for graph anomaly detection, achieving state-of-the-art results on six datasets.