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
  • A Timeline of graph anomaly detection
  • Surveys
  • Anomalous Node Detection
  • Anomalous Edge Detection
  • Anomalous Sub-graph Detection
  • Anomalous Graph-Level Detection
  • Graph-Based Anomaly Detection Methods
  • Open-sourced Graph Anomaly Detection Libraries
  • Datasets
    • Mostly-used Benchmark Datasets
    • Citation/Co-authorship Networks
    • Social Networks
    • Co-purchasing Networks
    • Transportation Networks
  • Tools

A Timeline of graph anomaly detection

Surveys

| Paper Title | Venue | Year | | --------------- | ---- | ---- | | A Comprehensive Survey on Graph Anomaly Detection with Deep Learning | TKDE | 2021 | | Deep learning for anomaly detection | ACM Comput. Surv. | 2021 | | Anomaly detection for big data using efficient techniques: A review | AIDE | 2021 | | Anomalous Example Detection in Deep Learning: A Survey | IEEE | 2021 | | Outlier detection: Methods, models, and classification |ACM Comput. Surv. | 2020 | | A comprehensive survey of anomaly detection techniques for high dimensional big data | J. Big Data | 2020 | | Machine learning techniques for network anomaly detection: A survey| Int. Conf. Inform. IoT Enabling Technol | 2020 | | Fraud detection: A systematic literature review of graph-based anomaly detection approaches | DSS | 2020 | | A comprehensive survey on network anomaly detection | Telecommun. Syst. | | A survey of deep learning-based network anomaly detection | Clust. Comput. | 2019 | Combining machine learning with knowledge engineering to detect fake news in social networks-a survey | AAAI | 2019 | | Deep learning for anomaly detection: A survey | arXiv | 2019 | | Anomaly detection in dynamic networks: A survey | Rev. Comput. Stat. | 2018 | | A survey on social media anomaly detection | SIGKDD | 2016 | | Graph based anomaly detection and description: A survey | Data Min. Knowl. Discovery | 2015 | | Anomaly detection in online social networks | Soc. Networks | 2014 | | A survey of outlier detection methods in network anomaly identification | Comput. J. | 2011 | | Anomaly detection: A survey |ACM Comput. Surv. | 2009 |

Anomalous Node Detection

<a href="https://ieeexplore.ieee.org/abstract/document/9565320" target="_blank"><img src="./pics/Anomalous_Node_Toy.png" width = "80%" height = "100%" alt="Anomaly_Node_Toy.png" align=center target="_blank"/></a>

| Paper Title | Venue | Year | Model | Code | | ------ | :----: | :--: | :----: | :----: | | ComGA: Community-Aware Attributed Graph Anomaly Detection | WSDM | 2022 | ComGA | [Code] | | Anomaly detection on attributed networks via contrastive self-supervised learning | TNNLS | 2021 | CoLA | [Code] | | Cross-domain graph anomaly detection | TNNLS | 2021 | <center>-</center> | <center>-</center> | | A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning | TNNLS | 2021 | PamFul | [Code] | | ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning | CIKM | 2021 | ANEMONE | [Code] | | Error-bounded Graph Anomaly Loss for GNNs | CIKM | 2021 | GAL | [Code] | | Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection | TKDE | 2021 | SL-GAD | [Code] | | Fraudre: Fraud detection dual-resistant to graph inconsistency and imbalance | ICDM | 2021 | Fraudre | [Code] | | Few-shot network anomaly detection via cross-network meta-learning | WWW | 2021 | <center>-</center> | <center>-</center> | | Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field | AAAI | 2021 | <center>-</center> | <center>-</center> | | One-class graph neural networks for anomaly detection in attributed networks | NCA | 2021 | OCGNN | [Code] | | Decoupling representation learning and classification for gnn-based anomaly detection | SIGIR | 2021 | DCI | [Code] | | Resgcn: Attention-based deep residual modeling for anomaly detection on attributed networks | ML | 2021 | Resgcn | [Code] | | Selective network discovery via deep reinforcement learning on embedded spaces | ANS | 2021 | NAC | - | | A deep multi-view framework for anomaly detection on attributed networks | TKDE | 2020 | ALARM | - | | Enhancing graph neural network-based fraud detectors against camouflaged fraudsters | CIKM | 2020 | CARE-GNN | [Code] | | Outlier resistant unsupervised deep architectures for attributed network embedding | WSDM | 2020 | DONE/AdONE | [Code] | | Gcn-based user representation learning for unifying robust recommendation and fraudster detection | SIGIR | 2020 | GraphRfi | - | | Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection | SIGIR | 2020 | GraphConsis | [Code]| | Inductive anomaly detection on attributed networks | IJCAI | 2020 | AEGIS | - | | Anomalydae: Dual autoencoder for anomaly detection on attributed networks | ICAPSP | 2020 | Anomalydae | [Code] | | Specae: Spectral autoencoder for anomaly detection in attributed networks | CIKM | 2019 | Specae | - | | A semi-supervised graph attentive network for financial fraud detection | ICDM | 2019 | SemiGNN | [Code] | | Deep anomaly detection on attributed networks | SDM | 2019 | Dominant | [[Code]](https://github.com/kaize0409/GCN_AnomalyDe