Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs

arXiv:2609.02965 · cs.SI, cs.LG, stat.ML · Submitted 2026-09-02 · Read on arXiv

cs.SI, cs.LG, stat.ML

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/uos-sis/base-stat-aug-ad

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

Terminology

• Explainability — 8× in this paper · explained in 3 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes

Sources

• DyG2Vec: Efficient Representation Learning for Dynamic Graphs
• TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
• Temporal Graph Networks for Deep Learning on Dynamic Graphs
• SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
• Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
• Inductive Representation Learning on Temporal Graphs

Related papers

• Linking Scalar-Intensity Language to Structural Polarization with Validated Signed-Network Measures
• Detection and Characterization of Coordinated Online Behavior: A Survey
• Omega-N: Interpretable Structural Node Descriptors and Their Applicability Domain
• Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection
• Transmission Neural Networks: Inhibitory and Excitatory Connections
• A family of graph GOSPA metrics for graphs with different sizes