A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction

arXiv:2606.12673 · cs.LG, cs.AI · Submitted 2026-06-10 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-06-10

Updated: 2026-08-29

Terminology

• Graph Anomaly Detection — 25× in this paper
• Clustering — 17× in this paper · explained in 2 episodes
• Heterogeneous Graph — 10× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Adjacency Matrix — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Graph Fourier Transform — 3× in this paper
• Latent Representation — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Singular Value Decomposition — 2× in this paper
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• Spectral Gap — 1× in this paper · explained in 5 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Spectral Networks and Locally Connected Networks on Graphs
• Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
• Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection

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