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Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.
| Date | Stars |
|---|---|
| 2026-07-31 | 384 |
| 2026-08-01 | 384 |
| 2026-08-06 | 383 |
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# Awesome-Deep-Graph-Anomaly-Detection
[](https://github.com/XiaoxiaoMa-MQ/Awesome-Deep-Graph-Anomaly-Detection)
[](https://github.com/XiaoxiaoMa-MQ/Awesome-Deep-Graph-Anomaly-Detection)
A collection of papers on deep learning for graph anomaly detection, and published algorithms and datasets.
- [Awesome-Deep-Graph-Anomaly-Detection](#awesome-deep-graph-anomaly-detection)
- [A Timeline of graph anomaly detection](#a-timeline-of-graph-anomaly-detection)
- [Surveys](#surveys)
- [Anomalous Node Detection](#anomalous-node-detection)
- [Anomalous Edge Detection](#anomalous-edge-detection)
- [Anomalous Sub-graph Detection](#anomalous-sub-graph-detection)
- [Anomalous Graph-Level Detection](#anomalous-graph-level-detection)
- [Graph-Based Anomaly Detection Methods](#graph-based-anomaly-detection-methods)
- [Open-sourced Graph Anomaly Detection Libraries](#open-sourced-graph-anomaly-detection-libraries)
- [Datasets](#datasets)
- [Mostly-used Benchmark Datasets](#mostly-used-benchmark-datasets)
- [Citation/Co-authorship Networks](#citationco-authorship-networks)
- [Social Networks](#social-networks)
- [Co-purchasing Networks](#co-purchasing-networks)
- [Transportation Networks](#transportation-networks)
- [Tools](#tools)
----------
## A Timeline of graph anomaly detection
[](https://ieeexplore.ieee.org/abstract/document/9565320)
## Surveys
| **Paper Title** | **Venue** | **Year** |
| --------------- | ---- | ---- |
| [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://ieeexplore.ieee.org/abstract/document/9565320) | _TKDE_ | 2021 |
| [Deep learning for anomaly detection](https://dl.acm.org/doi/pdf/10.1145/3439950) | _ACM Comput. Surv._ | 2021 |
| [Anomaly detection for big data using efficient techniques: A review](https://link.springer.com/chapter/10.1007/978-981-15-3514-7_79) | AIDE | 2021 |
| [Anomalous Example Detection in Deep Learning: A Survey](https://ieeexplore.ieee.org/iel7/6287639/8948470/09144212.pdf) | _IEEE_ | 2021 |
| [Outlier detection: Methods, models, and classification](https://dl.acm.org/doi/pdf/10.1145/3381028) |_ACM Comput. Surv._ | 2020 |
| [A comprehensive survey of anomaly detection techniques for high dimensional big data](https://link.springer.com/article/10.1186/s40537-020-00320-x) | _J. Big Data_ | 2020 |
| [Machine learning techniques for network anomaly detection: A survey](https://ieeexplore.ieee.org/iel7/9081868/9089428/09089465.pdf)| _Int. Conf. Inform. IoT Enabling Technol_ | 2020 |
| [Fraud detection: A systematic literature review of graph-based anomaly detection approaches](https://dl.acm.org/doi/10.1145/3172867) | _DSS_ | 2020 |
| [A comprehensive survey on network anomaly detection](https://dl.acm.org/doi/10.1145/3172867) | _Telecommun. Syst._ |
| [A survey of deep learning-based network anomaly detection](https://dl.acm.org/doi/10.1145/3172867) | _Clust. Comput._ | 2019 |
[Combining machine learning with knowledge engineering to detect fake news in social networks-a survey](https://dl.acm.org/doi/10.1145/3172867) | _AAAI_ | 2019 |
| [Deep learning for anomaly detection: A survey](https://arxiv.org/pdf/1901.03407.pdf) | _arXiv_ | 2019 |
| [Anomaly detection in dynamic networks: A survey](https://wires.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/wics.1347) | _Rev. Comput. Stat._ | 2018 |
| [A survey on social media anomaly detection](https://dl.acm.org/doi/pdf/10.1145/2980765.2980Excerpt of 20,490 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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