mlsanityData to 7 Oct 2026

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges

arXiv 2212.05532 ↗First posted 11 Dec 2022cs.LG cs.SIcited by 15

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Abstract (arXiv)

Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 56 of its 144 references that are in the map. First posted on arXiv 11 Dec 2022.

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56 of 144 references.

References · 56

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38 more
  1. 2016Learning Convolutional Neural Networks for Graphscited by 314
  2. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  3. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
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  9. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
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  18. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
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  34. 2021Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detectioncited by 21
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  37. 2020Dynamic Graph-Based Anomaly Detection in the Electrical Gridcited by 9
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Citing papers per year: 2023: 4; 2024: 4; 2025: 3; 2026: 4.

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  1. 2024FairGT: A Fairness-aware Graph Transformercited by 29
  2. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  3. 2023Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learningcited by 9
  4. 2024Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 9
  5. 2023Entropy Causal Graphs for Multivariate Time Series Anomaly Detectioncited by 6
  6. 2024Deep Outdated Fact Detection in Knowledge Graphscited by 6
  7. 2025Foundation Models for Anomaly Detection: Vision and Challengescited by 3
  8. 2023Balanced Graph Structure Information for Brain Disease Detectioncited by 1
  9. 2025LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detectioncited by 1
  10. 2026When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generationcited by 1
  11. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0
  12. 2025NeuroPathNet: Dynamic Path Trajectory Learning for Brain Functional Connectivity Analysiscited by 0
3 more
  1. 2026Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learningcited by 0
  2. 2026Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selectioncited by 0
  3. 2026GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challengescited by 0