mlsanityData to 7 Oct 2026

Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

arXiv 2209.14930 ↗First posted 29 Sep 2022cs.LG cs.AI cs.SIcited by 23

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Abstract (arXiv)

Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations. To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score anomalies appropriately. In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph type (i.e., static and dynamic), the anomaly type (i.e., node, edge, subgraph, and whole graph), and the network architecture (e.g., graph autoencoder, graph convolutional network). To the best of our knowledge, this survey is the first comprehensive review of graph anomaly detection methods based on GNNs.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 34 of its 66 references that are in the map. First posted on arXiv 29 Sep 2022.

Builds on

34 of 66 references.

References · 34

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22 more
  1. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  2. 2020Data Augmentation for Graph Neural Networkscited by 200
  3. 2014Graph-based Anomaly Detection and Description: A Surveycited by 182
  4. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
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  7. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 98
  8. 2020AnomalyDAE: Dual autoencoder for anomaly detection on attributed networkscited by 81
  9. 2021Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detectioncited by 67
  10. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
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  12. 2020Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphscited by 55
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  14. 2020On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insightscited by 53
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  17. 2021Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networkscited by 31
  18. 2020ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networkscited by 24
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  20. 2020H-VGRAE: A Hierarchical Stochastic Spatial-Temporal Embedding Method for Robust Anomaly Detection in Dynamic Networkscited by 6
  21. 2019Deep Structured Cross-Modal Anomaly Detectioncited by 4
  22. 2021Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networkscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 7; 2025: 7; 2026: 2.

23 papers.

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  1. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  2. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 15
  3. 2024Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 9
  4. 2023Graph Neural Networks based Log Anomaly Detection and Explanationcited by 8
  5. 2023Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detectioncited by 7
  6. 2023Graph Neural Network for spatiotemporal data: methods and applicationscited by 6
  7. 2025Towards Personalized Quantum Federated Learning for Anomaly Detectioncited by 4
  8. 2023Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurementcited by 2
  9. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  10. 2025Computationally-efficient Graph Modeling with Refined Graph Random Featurescited by 2
  11. 2023Label-based Graph Augmentation with Metapath for Graph Anomaly Detectioncited by 1
  12. 2024SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networkscited by 1
11 more
  1. 2026A General ReLearner: Empowering Spatiotemporal Prediction by Re-learning Input-label Residualcited by 1
  2. 2026Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimizationcited by 1
  3. 2023Self-supervised Learning for Anomaly Detection in Computational Workflowscited by 0
  4. 2024Robust Subgraph Learning by Monitoring Early Training Representationscited by 0
  5. 2024Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detectioncited by 0
  6. 2024A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphscited by 0
  7. 2024Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremescited by 0
  8. 2025Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learningcited by 0
  9. 2025Mathematical Insights into Protein Architecture: Persistent Homology and Machine Learning Applied to the Flagellar Motorcited by 0
  10. 2025Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspectivecited by 0
  11. 2025Robust Anomaly Detection with Graph Neural Networks using Controllabilitycited by 0