mlsanityData to 14 Oct 2026

LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

arXiv 2112.05355 ↗First posted 10 Dec 2021cs.LG cs.AIcited by 42

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Abstract (arXiv)

Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN. They are popular for their simple principles and strong performance on unstructured, feature-based data that is commonplace in many practical applications. However, they cannot learn to adapt for a particular set of data due to their lack of trainable parameters. In this paper, we begin by unifying local outlier methods by showing that they are particular cases of the more general message passing framework used in graph neural networks. This allows us to introduce learnability into local outlier methods, in the form of a neural network, for greater flexibility and expressivity: specifically, we propose LUNAR, a novel, graph neural network-based anomaly detection method. LUNAR learns to use information from the nearest neighbours of each node in a trainable way to find anomalies. We show that our method performs significantly better than existing local outlier methods, as well as state-of-the-art deep baselines. We also show that the performance of our method is much more robust to different settings of the local neighbourhood size.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 10 Dec 2021.

Builds on

15 of 42 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  4. 2019Graph Neural Networks for Social Recommendationcited by 714
  5. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  6. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  7. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
  8. 2018Latent Space Autoregression for Novelty Detectioncited by 149
  9. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 146
  10. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  11. 2020Deep Nearest Neighbor Anomaly Detectioncited by 104
  12. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
3 more
  1. 2020Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphscited by 56
  2. 2020Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failurecited by 27
  3. 2019Anomaly Detection with Adversarial Dual Autoencoderscited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 11; 2024: 8; 2025: 7; 2026: 11.

42 papers.

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  1. 2023On Diffusion Modeling for Anomaly Detectioncited by 72
  2. 2022Detecting Multivariate Time Series Anomalies with Zero Known Labelcited by 21
  3. 2023Unsupervised anomaly detection algorithms on real-world data: how many do we need?cited by 20
  4. 2024PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selectioncited by 17
  5. 2023The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognitioncited by 16
  6. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  7. 2023Label-Free Multivariate Time Series Anomaly Detectioncited by 14
  8. 2024Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directionscited by 13
  9. 2022AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detectioncited by 11
  10. 2024The OPS-SAT benchmark for detecting anomalies in satellite telemetrycited by 7
  11. 2025TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detectioncited by 6
  12. 2026Towards Anomaly Detection on Relational Datacited by 5
30 more
  1. 2022Anomaly detection using data depth: multivariate casecited by 4
  2. 2024Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectoriescited by 4
  3. 2026ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimescited by 4
  4. 2023Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detectioncited by 3
  5. 2022AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithmcited by 2
  6. 2024Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixturescited by 2
  7. 2026Towards Token-Level Text Anomaly Detectioncited by 2
  8. 2022ARES: Locally Adaptive Reconstruction-based Anomaly Scoringcited by 1
  9. 2023Zero-Shot Anomaly Detection via Batch Normalizationcited by 1
  10. 2023A Probabilistic Transformation of Distance-Based Outlierscited by 1
  11. 2023AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicinecited by 1
  12. 2024When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detectioncited by 1
  13. 2024Towards Unsupervised Validation of Anomaly-Detection Modelscited by 1
  14. 2025Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imagingcited by 1
  15. 2026UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Modelscited by 1
  16. 2023Image Outlier Detection Without Training using RANSACcited by 0
  17. 2023Boundary Peeling: Outlier Detection Method Using One-Class Peelingcited by 0
  18. 2024Rethinking Unsupervised Outlier Detection via Multiple Thresholdingcited by 0
  19. 2025Feature Explosion: a generic optimization strategy for outlier detection algorithmscited by 0
  20. 2025Robust Isolation Forest using Soft Sparse Random Projection and Valley Emphasis Methodcited by 0
  21. 2025Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detectioncited by 0
  22. 2025Label-Informed Outlier Detection Based on Granule Densitycited by 0
  23. 2025Outlier detection in mixed-attribute data: a semi-supervised approach with fuzzy approximations and relative entropycited by 0
  24. 2026Anatomy of a failure: When, how, and why deep vision fails in scientific domainscited by 0
  25. 2026When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detectioncited by 0
  26. 2026Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmarkcited by 0
  27. 2026Witnesses Explain Anomaliescited by 0
  28. 2026Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Datacited by 0
  29. 2026SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detectioncited by 0
  30. 2026DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detectioncited by 0