mlsanityData to 7 Oct 2026

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

arXiv 2206.10071 ↗First posted 21 Jun 2022cs.LG cs.SIcited by 80

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Abstract (arXiv)

Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years for this task, there has been no standard comprehensive setting for performance evaluation. Consequently, it has been difficult to understand which methods work well and when under a broad range of settings. To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights. (1) We benchmark the outlier detection performance of 14 methods ranging from classical matrix factorization to the latest graph neural networks. (2) Using nine real datasets, our benchmark assesses how the different detection methods respond to two major types of synthetic outliers and separately to "organic" (real non-synthetic) outliers. (3) Using an existing random graph generation technique, we produce a family of synthetically generated datasets of different graph sizes that enable us to compare the running time and memory usage of the different outlier detection algorithms. Based on our experimental results, we discuss the pros and cons of existing graph outlier detection algorithms, and we highlight opportunities for future research. Importantly, our code is freely available and meant to be easily extendable: https://github.com/pygod-team/pygod/tree/main/benchmark

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). Builds on 41 of its 106 references that are in the map. First posted on arXiv 21 Jun 2022.

Builds on

41 of 106 references.

References · 41

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  4. 2016Variational Graph Auto-Encoderscited by 1,373
  5. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  6. 2015Image-based Recommendations on Styles and Substitutescited by 702
  7. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  8. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  9. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  10. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  11. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
  12. 2020Can Graph Neural Networks Count Substructures?cited by 235
29 more
  1. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  2. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  3. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  4. 2014Graph-based Anomaly Detection and Description: A Surveycited by 182
  5. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  6. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  7. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 158
  8. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  9. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  10. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  11. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  12. 2020AnomalyDAE: Dual autoencoder for anomaly detection on attributed networkscited by 81
  13. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  14. 2020A Large-Scale Database for Graph Representation Learningcited by 54
  15. 2020On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insightscited by 53
  16. 2020Uncovering Coordinated Networks on Social Media: Methods and Case Studiescited by 49
  17. 2020Policy-GNN: Aggregation Optimization for Graph Neural Networkscited by 48
  18. 2022Raising the Bar in Graph-level Anomaly Detectioncited by 45
  19. 2021Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learningcited by 44
  20. 2020TODS: An Automated Time Series Outlier Detection Systemcited by 41
  21. 2021VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantizationcited by 39
  22. 2018LSCP: Locally Selective Combination in Parallel Outlier Ensemblescited by 31
  23. 2022PyGOD: A Python Library for Graph Outlier Detectioncited by 31
  24. 2018Outlier Aware Network Embedding for Attributed Networkscited by 29
  25. 2020Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Datacited by 25
  26. 2021Meta Propagation Networks for Graph Few-shot Semi-supervised Learningcited by 25
  27. 2022Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solutioncited by 24
  28. 2020Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approachcited by 20
  29. 2022Towards Similarity-Aware Time-Series Classificationcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 21; 2024: 26; 2025: 20; 2026: 9.

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  1. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 57
  2. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  3. 2023GAD-NR: Graph Anomaly Detection via Neighborhood Reconstructioncited by 44
  4. 2023Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detectioncited by 43
  5. 2024Deep Graph Anomaly Detection: A Survey and New Perspectivescited by 40
  6. 2024Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Promptscited by 24
  7. 2023ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detectioncited by 23
  8. 2023Weakly Supervised Anomaly Detection: A Surveycited by 22
  9. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  10. 2024Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspectivecited by 22
  11. 2024Uncertainty in Graph Neural Networks: A Surveycited by 20
  12. 2024Anomaly Detection in Dynamic Graphs: A Comprehensive Surveycited by 18
38 more
  1. 2024Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmarkcited by 18
  2. 2024NLP-ADBench: NLP Anomaly Detection Benchmarkcited by 17
  3. 2024PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selectioncited by 17
  4. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
  5. 2022Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Methodcited by 15
  6. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  7. 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysiscited by 13
  8. 2024UniGAD: Unifying Multi-level Graph Anomaly Detectioncited by 13
  9. 2025A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Modelscited by 13
  10. 2023Improving Generalizability of Graph Anomaly Detection Models via Data Augmentationcited by 11
  11. 2023ADGym: Design Choices for Deep Anomaly Detectioncited by 11
  12. 2024SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detectioncited by 11
  13. 2025FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detectioncited by 10
  14. 2024DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detectorcited by 9
  15. 2022ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awarenesscited by 8
  16. 2023Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detectioncited by 7
  17. 2024Multitask Active Learning for Graph Anomaly Detectioncited by 7
  18. 2024Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detectioncited by 7
  19. 2025Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspectivecited by 7
  20. 2023Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learningcited by 6
  21. 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluationcited by 6
  22. 2025OCR-APT: Reconstructing APT Stories from Audit Logs using Subgraph Anomaly Detection and LLMscited by 6
  23. 2023Subgraph Centralization: A Necessary Step for Graph Anomaly Detectioncited by 5
  24. 2023Dynamic Relation-Attentive Graph Neural Networks for Fraud Detectioncited by 5
  25. 2025Graph Evidential Learning for Anomaly Detectioncited by 5
  26. 2023Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Networkcited by 4
  27. 2024Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphscited by 4
  28. 2025CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detectioncited by 4
  29. 2025Semi-supervised Graph Anomaly Detection via Robust Homophily Learningcited by 4
  30. 2025Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learningcited by 4
  31. 2023GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Mapscited by 3
  32. 2024Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networkscited by 3
  33. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  34. 2023Anomaly Detection in Networks via Score-Based Generative Modelscited by 2
  35. 2024Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networkscited by 2
  36. 2026Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detectioncited by 2
  37. 2026CatchBench: When Can an Agent Failure Be Caught?cited by 2
  38. 2023Inclusive FinTech Lending via Contrastive Learning and Domain Adaptationcited by 1

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