DDGAD: Disagreement-Driven Graph Anomaly Detection via Adapt-Then-Combine

arXiv:2605.26446 · cs.LG, cs.AI · Submitted 2026-05-26 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-05-26

Updated: 2026-09-22

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 22× in this paper · explained in 1 episode
• Aggregation — 7× in this paper · explained in 1 episode
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• AUROC — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Disagreement-Driven Graph Anomaly Detection — 2× in this paper
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Memory Decay — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Anomaly Scoring Mechanism — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Inspired by Adapt-Then-Combine — 1× in this paper
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise

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