/Awesome-Deep-Graph-Anomaly-Detection

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.

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Awesome-Deep-Graph-Anomaly-Detection

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A collection of papers on deep learning for graph anomaly detection, and published algorithms and datasets.

A Timeline of graph anomaly detection

timeline

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

Anomalous Node Detection

Paper Title Venue Year Model Code
Anomaly detection on attributed networks via contrastive self-supervised learning TNNLS 2021 CoLA [Code]
Cross-domain graph anomaly detection TNNLS 2021 - -
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 - -
One-class graph neural networks for anomaly detection in attributed networks NCA 2021 OCGNN [Code]
Error-bounded graph anomaly loss for gnns CIKM 2021 - -
Decoupling representation learning and classification for gnn-based anomaly detection SIGIR 2021 DCI -
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]
One-class adversarial nets for fraud detection AAAI 2019 OCAN [Code]
Interactive anomaly detection on attributed networks WSDM 2019 GraphUCB -
Fdgars: Fraudster detection via graph convolutional networks in online app review system WWW 2019 Wang et al. [Code]
A robust embedding method for anomaly detection on attributed networks IJCNN 2019 REMAD -
Semi-supervised embedding in attributed networks with outliers SDM 2018 SEANO -
Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks SIGKDD 2018 Netwalk [Code]
Anomalous: A joint modeling approach for anomaly detection on attributed networks IJCAI 2018 ANOMALOUS -
Accelerated local anomaly detection via resolving attributed networks IJCAI 2017 ALAD -
Radar: Residual analysis for anomaly detection in attributed networks IJCAI 2017 Radar [Code]
Anomaly detection in dynamic networks using multi-view time-series hypersphere learning CIKM 2017 MTHL -
An embedding approach to anomaly detection ICDE 2016 - [Code]
Oddball: Spotting anomalies in weighted graphs PAKDD 2016 Oddball [Code]
Fraudar: Bounding graph fraud in the face of camouflage SIGKDD 2016 Fraudar [Code]
Intrusion as (anti)social communication: characterization and detection SIGKDD 2012 - -

Anomalous Edge Detection

Anomalous Edge detection

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]

Anomalous Sub-graph Detection

Anomalous SG Detection

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 -

Anomalous Graph-Level Detection

Anomalous G Detection

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]

Graph-Based Anomaly Detection Methods

Anomaly_GraphBased_Toy.png

Paper Title Venue Year Model Code
Dynamic Graph-Based Anomaly Detection in the Electrical Grid Trans. Power Syst. 2021 - -
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification SSDBM 2021 NF-GNN -

Open-sourced Graph Anomaly Detection Libraries

Library Link
pygod [Github]
DGFraud [Github]

Datasets

Mostly-used Benchmark Datasets

Citation/Co-authorship Networks

Social Networks

Co-purchasing Networks

Transportation Networks

Tools

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.