mlsanityData to 7 Oct 2026

A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems

arXiv 2206.04149 ↗First posted 8 Jun 2022cs.LGcited by 20

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Abstract (arXiv)

Anomaly detection is a crucial task in complex distributed systems. A thorough understanding of the requirements and challenges of anomaly detection is pivotal to the security of such systems, especially for real-world deployment. While there are many works and application domains that deal with this problem, few have attempted to provide an in-depth look at such systems. In this survey, we explore the potentials of graph-based algorithms to identify anomalies in distributed systems. These systems can be heterogeneous or homogeneous, which can result in distinct requirements. One of our objectives is to provide an in-depth look at graph-based approaches to conceptually analyze their capability to handle real-world challenges such as heterogeneity and dynamic structure. This study gives an overview of the State-of-the-Art (SotA) research articles in the field and compare and contrast their characteristics. To facilitate a more comprehensive understanding, we present three systems with varying abstractions as use cases. We examine the specific challenges involved in anomaly detection within such systems. Subsequently, we elucidate the efficacy of graphs in such systems and explicate their advantages. We then delve into the SotA methods and highlight their strength and weaknesses, pointing out the areas for possible improvements and future works.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 66 of its 159 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

66 of 159 references.

References · 66

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  4. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  5. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  7. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  8. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
  9. 2019Towards Federated Learning at Scale: System Designcited by 826
  10. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  11. 2015LINE: Large-scale Information Network Embeddingcited by 582
  12. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
38 more
  1. 2020Federated Learning with Matched Averagingcited by 564
  2. 2019Graph Transformer Networkscited by 520
  3. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  4. 2019Diffusion Improves Graph Learningcited by 414
  5. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  6. 2015Adversarial Autoencoderscited by 359
  7. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  8. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  9. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
  10. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  11. 2017Learning Explanatory Rules from Noisy Datacited by 210
  12. 2014Graph-based Anomaly Detection and Description: A Surveycited by 182
  13. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  14. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  15. 2019NLNL: Negative Learning for Noisy Labelscited by 151
  16. 2019Noisier2Noise: Learning to Denoise from Unpaired Noisy Datacited by 125
  17. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  18. 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
  19. 2020Benchmarking TinyML Systems: Challenges and Directioncited by 89
  20. 2020Quantifying Privacy Leakage in Graph Embeddingcited by 82
  21. 2021User Preference-aware Fake News Detectioncited by 76
  22. 2014Toward Supervised Anomaly Detectioncited by 73
  23. 2013Discrete Signal Processing on Graphs: Frequency Analysiscited by 72
  24. 2021Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detectioncited by 67
  25. 2020Locally Private Graph Neural Networkscited by 64
  26. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  27. 2020Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosiscited by 60
  28. 2021Anomaly Detection in Dynamic Graphs via Transformercited by 57
  29. 2021Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Surveycited by 47
  30. 2021Dynamic Network Embedding Surveycited by 44
  31. 2021Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformerscited by 41
  32. 2019Abnormal Client Behavior Detection in Federated Learningcited by 39
  33. 2019Robust Anomaly Detection in Images using Adversarial Autoencoderscited by 38
  34. 2019SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networkscited by 37
  35. 2018Robust Graph Learning from Noisy Datacited by 34
  36. 2021Federated Graph Learning -- A Position Papercited by 34
  37. 2014LSDA: Large Scale Detection Through Adaptationcited by 32
  38. 2018Anomaly Detection using Autoencoders in High Performance Computing Systemscited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 11; 2026: 2.

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  1. 2024An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Predictioncited by 19
  2. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  3. 2024Anomaly Detection in Dynamic Graphs: A Comprehensive Surveycited by 18
  4. 2025Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspectivecited by 7
  5. 2024Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmarkcited by 6
  6. 2025Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Posecited by 6
  7. 2023From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safetycited by 5
  8. 2025Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmarkcited by 5
  9. 2025Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structurescited by 4
  10. 2025Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learningcited by 4
  11. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  12. 2023Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studiescited by 3
8 more
  1. 2025Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detectioncited by 3
  2. 2025MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generationcited by 2
  3. 2024A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systemscited by 0
  4. 2025Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniquescited by 0
  5. 2025DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoTcited by 0
  6. 2025Graph Neural Network Based Adaptive Threat Detection for Cloud Identity and Access Management Logscited by 0
  7. 2026TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detectioncited by 0
  8. 2026A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstructioncited by 0