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.
View the Project on GitHub XiaoxiaoMa-MQ/Awesome-Deep-Graph-Anomaly-Detection
A collection of papers on deep learning for graph anomaly detection, and published algorithms and datasets.
Paper Title | Venue | Year | Model | Code |
---|---|---|---|---|
Anomaly Detection in Dynamic Graphs via Transformer | TKDE | 2021 | TADDY | [Code] |
efraudcom: An e-commerce fraud detection system via competitive graph neural networks | IS | 2021 | efraudcom | [Code] |
Unified graph embedding-based anomalous edge detection | IJCNN | 2020 | - | - |
AANE: Anomaly aware network embedding for anomalous link detection | ICDM | 2020 | AANE | - |
Addgraph: Anomaly detection in dynamic graph using attention-based temporal gcn | IJCAI | 2019 | Addgraph | - |
Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks | SIGKDD | 2018 | Netwalk | [Code] |
Paper Title | Venue | Year | Model | Code |
---|---|---|---|---|
SliceNDice: Mining suspicious multi-attribute entity groups with multi-view graphs | arXiv | 2020 | SliceNDice | [Code] |
Deep structure learning for fraud detection | ICDM | 2018 | DeepFD | [Code] |
Fraudne: A joint embedding approach for fraud detection | IJCNN | 2018 | FraudNE | - |
Paper Title | Venue | Year | Model | Code |
---|---|---|---|---|
User preference-aware fake news detection | SIGIR | 2021 | UPFD | [Code] |
On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights | arXiv | 2021 | OCGIN | [Code] |
Glad-paw: Graph-based log anomaly detection by position aware weighted graph attention network | PAKDD | 2021 | Glad-paw | - |
Deep into hypersphere: Robust and unsupervised anomaly discovery in dynamic networks | IJCAI | 2018 | DeepSphere | [Code] |
Paper Title | Venue | Year | Model | Code |
---|---|---|---|---|
Detecting rumours with latency guarantees using massive streaming data | VLDB J. | 2022 | - | - |
Graph Neural Networks for Anomaly Detection in Industrial Internet of Things | IEEE Internet of Things Journal | 2022 | - | - |
Dynamic Graph-Based Anomaly Detection in the Electrical Grid | Trans. Power Syst. | 2021 | - | [Code] |
Nonparametric Anomaly Detection on Time Series of Graphs | J. Comput. Graph. Stat. | 2021 | - | [Code] |
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification | SSDBM | 2021 | NF-GNN | - |
Library | Link |
---|---|
pygod | [Github] |
DGFraud | [Github] |
Disclaimer
If you have any questions or updated news on graph anomaly detection, please feel free to contact us. 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.
Emails: xiaoxiao.ma2@hdr.mq.edu.au, jia.wu@mq.edu.au.