Awesome-Deep-Graph-Anomaly-Detection
A collection of papers on deep learning for graph anomaly detection, and published algorithms and datasets.
A Timeline of graph anomaly detection
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning . 28 Pages , IEEE Trans. Knowl. Data Eng., 2021.
Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu , [Paper ]
Link: [https://ieeexplore.ieee.org/abstract/document/9565320 ]
@article{ma2021comprehensive,
title={A comprehensive survey on graph anomaly detection with
deep learning},
author={Ma, Xiaoxiao and Wu, Jia and Xue, Shan and Yang, Jian and
Zhou, Chuan and Sheng, Quan Z and Xiong, Hui and
Akoglu, Leman},
journal={IEEE Transactions on Knowledge and Data Engineering},
year={2021},
publisher={IEEE}
}
Paper Title
Venue
Year
Authors
Materials
Deep learning for anomaly detection
ACM Comput. Surv.
2021
Pang et al. [Paper ]
Anomaly detection for big data using efficient techniques: A review
AIDE
2021
Jennifer and Kumar [Paper ]
Anomalous Example Detection in Deep Learning: A Survey
IEEE
2021
Bulusu et al. [Paper ]
Outlier detection: Methods, models, and classification
ACM Comput. Surv.
2020
Boukerche et al. [Paper ]
A comprehensive survey of anomaly detection techniques for high dimensional big data
J. Big Data
2020
Thudumu et al. [Paper ]
Machine learning techniques for network anomaly detection: A survey
Int. Conf. Inform. IoT Enabling Technol
2020
Eltanbouly et al. [Paper ]
Fraud detec- tion: A systematic literature review of graph-based anomaly detection approaches
Decis. Support Syst.
2020
Pourhabibi et al. [Paper ]
A comprehensive survey on network anomaly detection
Telecommun. Syst.
2020
Fernandes et al. [Paper ]
A survey of deep learning-based network anomaly detection
Clust. Comput.
2019
Kwon et al. [Paper ]
Combining machine learning with knowledge engineering to detect fake news in social networks-a survey
AAAI Conf. Artif. Intell
2019
Hunkelmann et al. [Paper ]
Deep learning for anomaly detection: A survey
arXiv
2019
Chalapathy and Chawla [Paper ]
Anomaly detection in dynamic networks: A survey
Wiley Interdiscip. Rev. Comput. Stat.
2018
Ranshous et al. [Paper ]
A survey on social media anomaly detection
SIGKDD Explor.
2016
Yu et al. [Paper ]
Graph based anomaly detection and description: A survey
Data Min. Knowl. Discovery
2015
Akoglu et al. [Paper ]
Anomaly detection in online social networks
Soc. Networks
2014
Savage et al. [Paper ]
A survey of outlier detection methods in network anomaly identification
Comput. J.
2011
Gogoi et al. [Paper ]
Anomaly detection: A survey
ACM Comput. Surv.
2009
Chandola et al. [Paper ]
Anomalous Node Detection on Static Graphs Anomalous Node Detection on Static Plain Graphs Traditional Non-Deep Learning Techniques
Paper Title
Venue
Year
Authors
Materials
Oddball: Spotting anomalies in weighted graphs
Pacific-Asia Conf. Knowl. Discov. Data Mining.
2016
Akoglu et al. [Paper ]
Fraudar: Bounding graph fraud in the face of camouflage
ACM SIGKDD
2016
Hooi et al. [Paper ]
Intrusion as (anti)social communication: characterization and detection
ACM SIGKDD
2012
Ding et al. [Paper ]
Network Representation Based Techniques
Paper Title
Venue
Year
Authors
Materials
Decoupling representation learning and classification for gnn-based anomaly detection
Int. ACM SIGIR
2021
Wang et al. [Paper ]
An embedding approach to anomaly detection
Int. Conf. Data Eng.
2016
Hu et al. [Paper ]
Reinforcement Learning Based Techniques
Paper Title
Venue
Year
Authors
Materials
Selective network discovery via deep reinforcement learning on embedded spaces
Appl.Network Sci.
2021
Morales et al. [Paper ]
Anomalous Node Detection on Static Attributed Graphs Traditional Non-Deep Learning Techniques
Paper Title
Venue
Year
Authors
Materials
Anomalous: A joint modeling approach for anomaly detection on attributed networks
Int. Joint Conf. Artif. Intell.
2018
Peng et al. [Paper ]
Accelerated local anomaly detection via resolving attributed networks
Int. Joint Conf. Artif. Intell.
2017
Liu et al. [Paper ]
Radar: Residual analysis for anomaly detection in attributed networks
Int. Joint Conf. Artif. Intell.,
2017
Li et al. [Paper ]
Deep Neural Network Based Techniques
Paper Title
Venue
Year
Authors
Materials
Outlier resistant unsupervised deep architectures for attributed network embedding
Int. Conf. Web Search Data Mining
2020
Bandyopadhyay et al. [Paper ]
Graph Convolutional Neural Network Based Techniques
Paper Title
Venue
Year
Authors
Materials
Resgcn: Attention-based deep residual modeling for anomaly detection on attributed networks
Mach. Learn.
2021
Pei et al. [Paper ]
A deep multi-view framework for anomaly detection on attributed networks
IEEE Trans. Knowl. Data Eng.
2020
Peng et al. [Paper ]
Specae: Spectral autoencoder for anomaly detection in attributed networks
Int. Conf. Inf. Knowl. Manage.
2020
Li et al. [Paper ]
Gcn-based user representation learning for unifying robust recommendation and fraudster detection
ACM SIGIR
2020
Zhang et al. [Paper ]
Deep anomaly detection on attributed networks
SIAM Int. Conf. Data Mining
2019
Ding et al. [Paper ]
Fdgars: Fraudster detection via graph convolutional networks in online app review system
Int. Conf. World Wide Web
2019
Wang et al. [Paper ]
Graph Attention Neural Network Based Techniques
Paper Title
Venue
Year
Authors
Materials
Anomalydae: Dual autoencoder for anomaly detection on attributed networks
IEEE Int. Conf. Acoustics Speech Signal Processing
2020
Fan et al. [Paper ]
A semi-supervised graph attentive network for financial fraud detection
IEEE Int. Conf. Data Mining
2019
Wang et al. [Paper ]
Generative Adversarial Neural Network Based Techniques
Paper Title
Venue
Year
Authors
Materials
Inductive anomaly detection on attributed networks
Int. Joint Conf. Artif. Intell.
2020
Ding et al. [Paper ]
Reinforcement Learning Based Techniques
Paper Title
Venue
Year
Authors
Materials
Interactive anomaly detection on attributed networks
Int. Conf. Web Search Data Mining
2019
Ding et al. [Paper ]
Network Representation Based Techniques
Paper Title
Venue
Year
Authors
Materials
Anomaly detection on attributed networks via contrastive self-supervised learning
IEEE Trans. Neural Networks Learn. Syst.
2021
Liu et al. [Paper ]
Cross-domain graph anomaly detection
IEEE Trans. Neural Networks Learn. Syst.
2021
Ding et al. [Paper ]
Fraudre: Fraud detection dual-resistant to graph inconsistency and imbalance
ICDM
2021
Zhang et al. [Paper ]
Few-shot network anomaly detection via cross-network meta-learning
Web Conf.
2021
Ding et al. [Paper ]
One-class graph neural networks for anomaly detection in attributed networks
Neural Comput. Appl.
2021
Wang et al. [Paper ]
Error-bounded graph anomaly loss for gnns
ACM Int. Conf. Inf. Knowl. Manage.
2021
Zhao et al. [Paper ]
Enhancing graph neural network-based fraud detectors against camouflaged fraudsters
ACM Int. Conf. Inf. Knowl. Manage.
2020
Dou et al. [Paper ]
A robust embedding method for anomaly detection on attributed networks
Int. Joint Conf. Neural Netw.
2019
Zhang et al. [Paper ]
Semi-supervised embedding in attributed networks with outliers
SIAM Int. Conf. Data Mining
2018
Liang et al. [Paper ]
Anomalous Node Detection on Dynamic Graphs
Paper Title
Venue
Year
Authors
Materials
One-class adversarial nets for fraud detection
AAAI Conf. Artif. Intell.
2019
Zheng et al. [Paper ]
Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks
ACM SIGKDD
2018
Yu et al. [Paper ]
Anomaly detection in dynamic networks using multi-view time-series hypersphere learning
ACM Int. Conf. Inf. Knowl. Manage.
2017
Teng et al. [Paper ]
Paper Title
Venue
Year
Authors
Materials
efraudcom: An e-commerce fraud detection system via competitive graph neural networks
ACM Trans. Inf. Syst.
2021
Zhang et al. [Paper ]
Unified graph embedding-based anomalous edge detection
Int. Joint Conf. Neural Netw.
2020
Ouyang et al. [Paper ]
Aane: Anomaly aware network embedding for anomalous link detection
IEEE Int. Conf. Data Mining
2020
Duan et al. [Paper ]
Addgraph: Anomaly detection in dynamic graph using attention-based temporal gcn
Int. Joint Conf. Artif. Intell.
2019
Zheng et al. [Paper ]
Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks
ACM SIGKDD
2018
Yu et al. [Paper ]
Anomalous Sub-graph Detection
Paper Title
Venue
Year
Authors
Materials
Deep structure learning for fraud detection
IEEE Int. Conf. Data Mining
2018
Wang et al. [Paper ]
Fraudne: A joint embedding approach for fraud detection
Int. Joint Conf. Neural Netw.
2018
Zheng et al. [Paper ]
Anomalous Graph Detection
Paper Title
Venue
Year
Authors
Materials
User preference-aware fake news detection
arXiv
2021
Dou et al. [Paper ]
On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights
arXiv
2021
Zheng et al. [Paper ]
Glad-paw: Graph-based log anomaly detection by position aware weighted graph attention network
Pacific-Asia Conf. Knowl. Discov. Data Mining
2021
Zheng et al. [Paper ]
Deep into hypersphere: Robust and unsupervised anomaly discovery in dynamic networks
Int. Joint Conf. Artif. Intell.
2018
Teng et al. [Paper ]
Published Algorithms and Models Open-sourced Graph Anomaly Detection Libraries Mostly-used Benchmark Datasets Citation/Co-authorship Networks 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 .