mlsanityData to 14 Oct 2026

Anomaly Detection in Graph Structured Data: A Survey

arXiv 2405.06172 ↗First posted 10 May 2024cs.LG cs.CRcited by 3

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Abstract (arXiv)

Real-world graphs are complex to process for performing effective analysis, such as anomaly detection. However, recently, there have been several research efforts addressing the issues surrounding graph-based anomaly detection. In this paper, we discuss a comprehensive overview of anomaly detection techniques on graph data. We also discuss the various application domains which use those anomaly detection techniques. We present a new taxonomy that categorizes the different state-of-the-art anomaly detection methods based on assumptions and techniques. Within each category, we discuss the fundamental research ideas that have been done to improve anomaly detection. We further discuss the advantages and disadvantages of current anomaly detection techniques. Finally, we present potential future research directions in anomaly detection on graph-structured data.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 120 references that are in the map. First posted on arXiv 10 May 2024.

Builds on

33 of 120 references.

References · 33

  1. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  2. 2014Graph-based Anomaly Detection and Description: A Surveycited by 183
  3. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  4. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 153
  5. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 151
  6. 2020GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Mediacited by 109
  7. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 96
  8. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  9. 2020Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detectioncited by 82
  10. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
  11. 2021Anomaly Detection in Dynamic Graphs via Transformercited by 59
  12. 2020Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphscited by 56
21 more
  1. 2020GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detectioncited by 54
  2. 2020On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insightscited by 53
  3. 2022Raising the Bar in Graph-level Anomaly Detectioncited by 45
  4. 2016Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphscited by 37
  5. 2019SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networkscited by 37
  6. 2016Scalable Anomaly Ranking of Attributed Neighborhoodscited by 33
  7. 2018One-Class Adversarial Nets for Fraud Detectioncited by 33
  8. 2019MIDAS: Microcluster-Based Detector of Anomalies in Edge Streamscited by 29
  9. 2020F-FADE: Frequency Factorization for Anomaly Detection in Edge Streamscited by 26
  10. 2020Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approachcited by 20
  11. 2016A Survey on Social Media Anomaly Detectioncited by 17
  12. 2020MSTREAM: Fast Anomaly Detection in Multi-Aspect Streamscited by 17
  13. 2016Anomaly detection in online social networkscited by 15
  14. 2014Detecting change points in the large-scale structure of evolving networkscited by 13
  15. 2020Mining Persistent Activity in Continually Evolving Networkscited by 13
  16. 2020Improving Cyberbully Detection with User Interactioncited by 10
  17. 2020Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detectioncited by 8
  18. 2020Real-Time Anomaly Detection in Edge Streamscited by 7
  19. 2014Spotting Suspicious Link Behavior with fBox: An Adversarial Perspectivecited by 6
  20. 2020H-VGRAE: A Hierarchical Stochastic Spatial-Temporal Embedding Method for Robust Anomaly Detection in Dynamic Networkscited by 6
  21. 2021Isconna: Streaming Anomaly Detection with Frequency and Patternscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communicationcited by 0
  2. 2025A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detectioncited by 0
  3. 2026Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attentioncited by 0