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This paper introduces RelAD, a reconstruction-based framework for detecting anomalies in relational databases by jointly modeling attribute and relational edge reconstruction. Extensive experiments on six new benchmarks show RelAD outperforms existing methods.
The Controlled Dynamics Attractor Transformer (CDAT) combines a mixture von Mises-Fisher attention energy with a Hopfield refinement energy and CANN-inspired excitation-inhibition modulation, providing topology-constrained dynamical systems for stable inference. It achieves state-of-the-art performance on graph anomaly detection and classification benchmarks.
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.