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
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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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