mlsanityData to 7 Oct 2026

Anomaly Detection in Dynamic Graphs via Transformer

arXiv 2106.09876 ↗First posted 18 Jun 2021cs.LGcited by 57

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Abstract (arXiv)

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. Recent deep learning-based approaches have shown promising results over shallow methods. However, they fail to address two core challenges of anomaly detection in dynamic graphs: the lack of informative encoding for unattributed nodes and the difficulty of learning discriminate knowledge from coupled spatial-temporal dynamic graphs. To overcome these challenges, in this paper, we present a novel Transformer-based Anomaly Detection framework for DYnamic graphs (TADDY). Our framework constructs a comprehensive node encoding strategy to better represent each node's structural and temporal roles in an evolving graphs stream. Meanwhile, TADDY captures informative representation from dynamic graphs with coupled spatial-temporal patterns via a dynamic graph transformer model. The extensive experimental results demonstrate that our proposed TADDY framework outperforms the state-of-the-art methods by a large margin on six real-world datasets.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). Builds on 29 of its 58 references that are in the map. First posted on arXiv 18 Jun 2021.

Builds on

29 of 58 references.

References · 29

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17 more
  1. 2020Efficient Transformers: A Surveycited by 784
  2. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
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  12. 2020A Universal Representation Transformer Layer for Few-Shot Image Classificationcited by 65
  13. 2020Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphscited by 55
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 8; 2023: 10; 2024: 18; 2025: 11; 2026: 9.

57 papers.

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  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  2. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  3. 2024Deep Graph Anomaly Detection: A Survey and New Perspectivescited by 40
  4. 2024Graph Transformers: A Surveycited by 32
  5. 2022Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysiscited by 28
  6. 2023SAD: Semi-Supervised Anomaly Detection on Dynamic Graphscited by 27
  7. 2022Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlookcited by 24
  8. 2024Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Promptscited by 24
  9. 2023Label Information Enhanced Fraud Detection against Low Homophily in Graphscited by 22
  10. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  11. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  12. 2024Anomaly Detection in Dynamic Graphs: A Comprehensive Surveycited by 18
38 more
  1. 2021TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecastingcited by 16
  2. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  3. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  4. 2024SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learningcited by 14
  5. 2022From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approachcited by 13
  6. 2024A Generalizable Anomaly Detection Method in Dynamic Graphscited by 10
  7. 2024STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecastingcited by 9
  8. 2022Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Predictioncited by 6
  9. 2023TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detectioncited by 6
  10. 2024Supra-Laplacian Encoding for Transformer on Dynamic Graphscited by 6
  11. 2024AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Modelscited by 5
  12. 2025Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructurescited by 4
  13. 2025DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypescited by 4
  14. 2026Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspectivecited by 4
  15. 2023Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networkscited by 3
  16. 2023GADY: Unsupervised Anomaly Detection on Dynamic Graphscited by 3
  17. 2024Anomaly Detection in Graph Structured Data: A Surveycited by 3
  18. 2024A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformerscited by 3
  19. 2025UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphscited by 3
  20. 2023Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Launderingcited by 2
  21. 2023Restricted Generative Projection for One-Class Classification and Anomaly Detectioncited by 2
  22. 2025Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphscited by 2
  23. 2025Towards Efficient LLM-aware Heterogeneous Graph Learningcited by 2
  24. 2023IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modelingcited by 1
  25. 2024RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detectioncited by 1
  26. 2024Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretabilitycited by 1
  27. 2024Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphscited by 1
  28. 2025Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformercited by 1
  29. 2025Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphscited by 1
  30. 2022tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphscited by 0
  31. 2024Detecting Anomalies in Dynamic Graphs via Memory enhanced Normalitycited by 0
  32. 2024Temporal receptive field in dynamic graph learning: A comprehensive analysiscited by 0
  33. 2024A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphscited by 0
  34. 2025A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networkscited by 0
  35. 2025Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphscited by 0
  36. 2025Robust Anomaly Detection Under Normality Distribution Shift in Dynamic Graphscited by 0
  37. 2025A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detectioncited by 0
  38. 2026Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervisioncited by 0

50 most cited of 57 All 57 (JSON)