Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts

arXiv:2602.06859 · cs.LG, cs.AI · Submitted 2026-02-06 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-02-06

Updated: 2026-08-29

Comments: Accepted by IEEE International Conference on Data Mining (ICDM 2026)

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

Terminology

• Graph Anomaly Detection — 21× in this paper
• Embeddings — 18× in this paper · explained in 2 episodes
• Alignment — 17× in this paper · explained in 1 episode
• Generalization — 17× in this paper · explained in 13 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• GNNs — 5× in this paper · explained in 2 episodes
• Reconstruction Error — 5× in this paper · explained in 2 episodes
• Adjacency Matrix — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Mixture of Experts — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Core — 3× in this paper · explained in 2 episodes
• Feature Selection — 3× in this paper · explained in 4 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Mixture-of-Experts (MoE) — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Mechanism — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Geometric Mismatch — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Principal Component Analysis — 1× in this paper
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

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