mlsanityData to 14 Oct 2026

PyGOD: A Python Library for Graph Outlier Detection

arXiv 2204.12095 ↗First posted 26 Apr 2022cs.LG cs.SIcited by 32

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Abstract (arXiv)

PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for outlier detection under an easy-to-use, well-documented API designed for use by both researchers and practitioners. PyGOD provides modularized components of the different detectors implemented so that users can easily customize each detector for their purposes. To ease the construction of detection workflows, PyGOD offers numerous commonly used utility functions. To scale computation to large graphs, PyGOD supports functionalities for deep models such as sampling and mini-batch processing. PyGOD uses best practices in fostering code reliability and maintainability, including unit testing, continuous integration, and code coverage. To facilitate accessibility, PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI).

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 22 of its 52 references that are in the map. First posted on arXiv 26 Apr 2022.

Builds on

22 of 52 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2016Variational Graph Auto-Encoderscited by 1,382
  5. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
  6. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  7. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 264
  8. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
  9. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  10. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
  11. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  12. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 167
10 more
  1. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  2. 2020AnomalyDAE: Dual autoencoder for anomaly detection on attributed networkscited by 82
  3. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  4. 2020Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphscited by 56
  5. 2020MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimizationcited by 49
  6. 2020TODS: An Automated Time Series Outlier Detection Systemcited by 41
  7. 2018Outlier Aware Network Embedding for Attributed Networkscited by 29
  8. 2020PyODDS: An End-to-end Outlier Detection System with Automated Machine Learningcited by 21
  9. 2021TOD: GPU-accelerated Outlier Detection via Tensor Operationscited by 6
  10. 2020Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Descriptioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 6; 2023: 6; 2024: 9; 2025: 8; 2026: 3.

32 papers.

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  1. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  2. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  3. 2022DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detectioncited by 63
  4. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 52
  5. 2023GAD-NR: Graph Anomaly Detection via Neighborhood Reconstructioncited by 45
  6. 2022TwiBot-22: Towards Graph-Based Twitter Bot Detectioncited by 44
  7. 2024AD-LLM: Benchmarking Large Language Models for Anomaly Detectioncited by 26
  8. 2023ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detectioncited by 23
  9. 2024Uncertainty in Graph Neural Networks: A Surveycited by 23
  10. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  11. 2025A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Modelscited by 14
  12. 2025FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detectioncited by 10
20 more
  1. 2024DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detectorcited by 9
  2. 2025AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detectioncited by 8
  3. 2022Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view informationcited by 7
  4. 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluationcited by 6
  5. 2025OCR-APT: Reconstructing APT Stories from Audit Logs using Subgraph Anomaly Detection and LLMscited by 6
  6. 2023Revisiting Graph Contrastive Learning for Anomaly Detectioncited by 4
  7. 2024Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detectioncited by 3
  8. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  9. 2025How to Use Graph Data in the Wild to Help Graph Anomaly Detection?cited by 3
  10. 2023Anomaly Detection in Networks via Score-Based Generative Modelscited by 2
  11. 2024Critical Review for One-class Classification: recent advances and the reality behind themcited by 2
  12. 2025PyGDA: A Python Library for Graph Domain Adaptationcited by 2
  13. 2022AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approachcited by 1
  14. 2023MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detectioncited by 1
  15. 2023Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Modelscited by 1
  16. 2024TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scalecited by 1
  17. 2025Combining Euclidean and Hyperbolic Representations for Node-level Anomaly Detectioncited by 0
  18. 2026Modeling Topological Impact on Node Attribute Distributions in Attributed Graphscited by 0
  19. 2026Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayescited by 0
  20. 2026Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphscited by 0