Anomaly Detection Learning Resources

Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.

This repository collects:

Books & Academic Papers
Online Courses and Videos
Outlier Datasets
Open-source and Commercial Libraries/Toolkits
Key Conferences & Journals

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ (yzhao010@usc.edu). Enjoy reading!

BTW, you may find my [GitHub] and [outlier detection papers] useful, especially PyOD library, ADBench benchmark, and AD-LLM: benchmarking LLMs for AD.

1. Books & Tutorials & Benchmarks

1.1. Books

Outlier Analysis by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. A must-read for people in the field of outlier detection. [Preview.pdf]

Outlier Ensembles: An Introduction by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.

Data Mining: Concepts and Techniques (3rd) by Jiawei Han and Micheline Kamber and Jian Pei: Chapter 12 discusses outlier detection with many key points. [Google Search]

1.2. Tutorials

Tutorial Title	Venue	Year	Ref	Materials
Data mining for anomaly detection	PKDD	2008	[47]	[Video]
Outlier detection techniques	ACM SIGKDD	2010	[41]	[PDF]
Which Outlier Detector Should I use?	ICDM	2018	[92]	[PDF]
Deep Learning for Anomaly Detection	KDD	2020	[96]	[HTML], [Video]
Deep Learning for Anomaly Detection	WSDM	2021	[72]	[HTML]
Toward Explainable Deep Anomaly Detection	KDD	2021	[73]	[HTML]
Recent Advances in Anomaly Detection	CVPR	2023	[74]	[HTML], [Video]
Trustworthy Anomaly Detection	SDM	2024	[108]	[HTML]

1.3. Benchmarks

News: We have two new works on NLP-based and LLM-based anomaly detection:

NLP-ADBench: NLP Anomaly Detection Benchmark
AD-LLM: Benchmarking Large Language Models for Anomaly Detection

Data Types	Paper Title	Venue	Year	Ref	Materials
Time-series	Revisiting Time Series Outlier Detection: Definitions and Benchmarks	NeurIPS	2021	[44]	[PDF], [Code]
Graph	Benchmarking Node Outlier Detection on Graphs	NeurIPS	2022	[60]	[PDF], [Code]
Graph	GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection	NeurIPS	2023	[91]	[PDF], [Code]
Tabular	ADBench: Anomaly Detection Benchmark	NeurIPS	2022	[67]	[PDF], [Code]
Tabular	ADGym: Design Choices for Deep Anomaly Detection	NeurIPS	2023	[42]	[PDF], [Code]
NLP	NLP-ADBench: NLP Anomaly Detection Benchmark	Preprint	2024	[53]	[PDF], [Code]
NLP	AD-LLM: Benchmarking Large Language Models for Anomaly Detection	Preprint	2024	[54]	[PDF], [Code]

2. Courses/Seminars/Videos

Coursera Introduction to Anomaly Detection (by IBM): [See Video]

Get started with the Anomaly Detection API (by IBM): [See Website]

Practical Anomaly Detection by appliedAI Institute: [See Website], [See Video], [See GitHub]

Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic: [See Video]

Coursera Machine Learning by Andrew Ng also partly covers the topic:

Udemy Outlier Detection Algorithms in Data Mining and Data Science: [See Video]

Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques: [See Video]

3. Toolbox & Datasets

3.1. Multivariate Data

[Python] Python Outlier Detection (PyOD): PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles.

[Python, GPU] TOD: Tensor-based Outlier Detection (PyTOD): A general GPU-accelerated framework for outlier detection.

[Python] Python Streaming Anomaly Detection (PySAD): PySAD is a streaming anomaly detection framework in Python, which provides a complete set of tools for anomaly detection experiments. It currently contains more than 15 online anomaly detection algorithms and 2 different methods to integrate PyOD detectors to the streaming setting.

[Python] Scikit-learn Novelty and Outlier Detection. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM.

[Python] Scalable Unsupervised Outlier Detection (SUOD): SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD.

[Julia] OutlierDetection.jl: OutlierDetection.jl is a Julia toolkit for detecting outlying objects, also known as anomalies.

[Java] ELKI: Environment for Developing KDD-Applications Supported by Index-Structures: ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection.

[Java] RapidMiner Anomaly Detection Extension: The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled.

[R] CRAN Task View: Anomaly Detection with R: This CRAN task view contains a list of packages that can be used for anomaly detection with R.

[R] outliers package: A collection of some tests commonly used for identifying outliers in R.

[Matlab] Anomaly Detection Toolbox - Beta: A collection of popular outlier detection algorithms in Matlab.

3.2. Time Series Outlier Detection

[Python] TODS: TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.

[Python] skyline: Skyline is a near real time anomaly detection system.

[Python] banpei: Banpei is a Python package of the anomaly detection.

[Python] telemanom: A framework for using LSTMs to detect anomalies in multivariate time series data.

[Python] DeepADoTS: A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods.

[Python] NAB: The Numenta Anomaly Benchmark: NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.

[Python] CueObserve: Anomaly detection on SQL data warehouses and databases.

[Python] Chaos Genius: ML powered analytics engine for outlier/anomaly detection and root cause analysis.

[R] CRAN Task View: Anomaly Detection with R: This CRAN task view contains a list of packages that can be used for anomaly detection with R.

[R] AnomalyDetection: AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend.

[R] anomalize: The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.

3.3. Graph Outlier Detection

[Python] Python Graph Outlier Detection (PyGOD): PyGOD is a Python library for graph outlier detection (anomaly detection). It includes more than 10 latest graph-based detection algorithms

3.4. Real-time Elasticsearch

[Open Distro] Real Time Anomaly Detection in Open Distro for Elasticsearch by Amazon: A machine learning-based anomaly detection plugins for Open Distro for Elasticsearch. See Real Time Anomaly Detection in Open Distro for Elasticsearch.

[Python] datastream.io: An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.

3.5. Datasets

NLP-ADBench: NLP Anomaly Detection Benchmark and Datasets: https://github.com/USC-FORTIS/NLP-ADBench

ELKI Outlier Datasets: https://elki-project.github.io/datasets/outlier

Outlier Detection DataSets (ODDS): http://odds.cs.stonybrook.edu/#table1

Unsupervised Anomaly Detection Dataverse: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF

Anomaly Detection Meta-Analysis Benchmarks: https://ir.library.oregonstate.edu/concern/datasets/47429f155

Skoltech Anomaly Benchmark (SKAB): https://github.com/waico/skab

4. Papers

4.1. Overview & Survey Papers

Papers are sorted by the publication year.

Paper Title	Venue	Year	Ref	Materials
A survey of outlier detection methodologies	ARTIF INTELL REV	2004	[37]	[PDF]
Anomaly detection: A survey	CSUR	2009	[19]	[PDF]
A meta-analysis of the anomaly detection problem	Preprint	2015	[29]	[PDF]
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study	DMKD	2016	[14]	[HTML], [SLIDES]
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data	PLOS ONE	2016	[33]	[PDF]
A comparative evaluation of outlier detection algorithms: Experiments and analyses	Pattern Recognition	2018	[28]	[PDF]
Research Issues in Outlier Detection	Book Chapter	2019	[89]	[HTML]
Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection	SAC	2019	[31]	[HTML]
Progress in Outlier Detection Techniques: A Survey	IEEE Access	2019	[95]	[PDF]
Deep learning for anomaly detection: A survey	Preprint	2019	[18]	[PDF]
Anomalous Instance Detection in Deep Learning: A Survey	Tech Report	2020	[13]	[PDF]
Anomaly detection in univariate time-series: A survey on the state-of-the-art	Preprint	2020	[11]	[PDF]
Deep Learning for Anomaly Detection: A Review	CSUR	2021	[71]	[PDF]
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning	TKDE	2021	[61]	[PDF]
Revisiting Time Series Outlier Detection: Definitions and Benchmarks	NeurIPS	2021	[44]	[PDF], [Code]
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges	Preprint	2021	[83]	[PDF]
Self-Supervised Anomaly Detection: A Survey and Outlook	Preprint	2022	[38]	[PDF]
Weakly supervised anomaly detection: A survey	Preprint	2023	[43]	[PDF], [PDF]
AD-LLM: Benchmarking Large Language Models for Anomaly Detection	Preprint	2024	[54]	[PDF], [Code]
Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey	Preprint	2024	[101]	[PDF]

4.2. Key Algorithms

All these algorithms are available in Python Outlier Detection (PyOD).

Abbreviation	Paper Title	Venue	Year	Ref	Materials
kNN	Efficient algorithms for mining outliers from large data sets	ACM SIGMOD Record	2000	[77]	[PDF]
KNN	Fast outlier detection in high dimensional spaces	PKDD	2002	[6]	[PDF]
LOF	LOF: identifying density-based local outliers	ACM SIGMOD Record	2000	[12]	[PDF]
IForest	Isolation forest	ICDM	2008	[55]	[PDF]
OCSVM	Estimating the support of a high-dimensional distribution	Neural Computation	2001	[84]	[PDF]
AutoEncoder Ensemble	Outlier detection with autoencoder ensembles	SDM	2017	[21]	[PDF]
COPOD	COPOD: Copula-Based Outlier Detection	ICDM	2020	[49]	[PDF]
ECOD	Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions	TKDE	2022	[50]	[PDF]

4.3. Graph & Network Outlier Detection

Paper Title	Venue	Year	Ref	Materials
Graph based anomaly detection and description: a survey	DMKD	2015	[5]	[PDF]
Anomaly detection in dynamic networks: a survey	WIREs Computational Statistic	2015	[78]	[PDF]
Outlier detection in graphs: On the impact of multiple graph models	ComSIS	2019	[16]	[PDF]
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning	TKDE	2021	[61]	[PDF]

4.4. Time Series Outlier Detection

Paper Title	Venue	Year	Ref	Materials
Outlier detection for temporal data: A survey	TKDE	2014	[34]	[PDF]
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding	KDD	2018	[39]	[PDF], [Code]
Time-Series Anomaly Detection Service at Microsoft	KDD	2019	[79]	[PDF]
Revisiting Time Series Outlier Detection: Definitions and Benchmarks	NeurIPS	2021	[44]	[PDF], [Code]
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series	ICLR	2022	[22]	[PDF], [Code]
Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection	NeurIPS	2023	[121]	[PDF], [Code]

4.5. Feature Selection in Outlier Detection

Paper Title	Venue	Year	Ref	Materials
Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings	ICDM	2016	[68]	[PDF]
Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection	IJCAI	2017	[69]	[PDF]

4.6. High-dimensional & Subspace Outliers

Paper Title	Venue	Year	Ref	Materials
A survey on unsupervised outlier detection in high-dimensional numerical data	Stat Anal Data Min	2012	[118]	[HTML]
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection	SIGKDD	2018	[70]	[PDF]
Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection	TKDE	2015	[76]	[PDF], [SLIDES]
Outlier detection for high-dimensional data	Biometrika	2015	[81]	[PDF]

4.7. Outlier Ensembles

Paper Title	Venue	Year	Ref	Materials
Outlier ensembles: position paper	SIGKDD Explorations	2013	[2]	[PDF]
Ensembles for unsupervised outlier detection: challenges and research questions a position paper	SIGKDD Explorations	2014	[119]	[PDF]
An Unsupervised Boosting Strategy for Outlier Detection Ensembles	PAKDD	2018	[15]	[HTML]
LSCP: Locally selective combination in parallel outlier ensembles	SDM	2019	[111]	[PDF]
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream	KDD	2022	[105]	[PDF], [Github], [Slide]

4.8. Outlier Detection in Evolving Data

Paper Title	Venue	Year	Ref	Materials
A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]	SIGKDD Explorations	2018	[82]	[PDF]
Unsupervised real-time anomaly detection for streaming data	Neurocomputing	2017	[4]	[PDF]
Outlier Detection in Feature-Evolving Data Streams	SIGKDD	2018	[63]	[PDF], [Github]
Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark	ICMLA	2015	[46]	[PDF], [Github]
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams	AAAI	2020	[10]	[PDF], [Github]
NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing	VLDB	2019	[102]	[PDF], [Github], [Slide]
Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping	KDD	2020	[103]	[PDF], [Github], [Slide]
Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries	SIGMOD	2021	[104]	[PDF], [Github], [Slide]
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream	KDD	2022	[105]	[PDF], [Github], [Slide]

4.9. Representation Learning in Outlier Detection

Paper Title	Venue	Year	Ref	Materials
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection	SIGKDD	2018	[70]	[PDF]
Learning representations for outlier detection on a budget	Preprint	2015	[65]	[PDF]
XGBOD: improving supervised outlier detection with unsupervised representation learning	IJCNN	2018	[110]	[PDF]

4.10. Interpretability

Paper Title	Venue	Year	Ref	Materials
Explaining Anomalies in Groups with Characterizing Subspace Rules	DMKD	2018	[62]	[PDF]
Beyond Outlier Detection: LookOut for Pictorial Explanation	ECML-PKDD	2018	[66]	[PDF]
Contextual outlier interpretation	IJCAI	2018	[57]	[PDF]
Mining multidimensional contextual outliers from categorical relational data	IDA	2015	[90]	[PDF]
Discriminative features for identifying and interpreting outliers	ICDE	2014	[23]	[PDF]
Sequential Feature Explanations for Anomaly Detection	TKDD	2019	[87]	[HTML]
A Survey on Explainable Anomaly Detection	TKDD	2023	[51]	[HTML]
Explainable Contextual Anomaly Detection Using Quantile Regression Forests	DMKD	2023	[52]	[HTML]
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network	WWW	2021	[98]	[PDF]

4.11. Outlier Detection with Neural Networks

Paper Title	Venue	Year	Ref	Materials
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding	KDD	2018	[39]	[PDF], [Code]
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks	ICANN	2019	[48]	[PDF], [Code]
Generative Adversarial Active Learning for Unsupervised Outlier Detection	TKDE	2019	[58]	[PDF], [Code]
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	ICLR	2018	[120]	[PDF], [Code]
Deep Anomaly Detection with Outlier Exposure	ICLR	2019	[36]	[PDF], [Code]
Unsupervised Anomaly Detection With LSTM Neural Networks	TNNLS	2019	[30]	[PDF], [IEEE],
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network	NeurIPS	2019	[94]	[PDF] [Code]
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning	ICML	2023	[100]	[PDF], [Code]

4.12. Active Anomaly Detection

Paper Title	Venue	Year	Ref	Materials
Active learning for anomaly and rare-category detection	NeurIPS	2005	[75]	[PDF]
Outlier detection by active learning	SIGKDD	2006	[1]	[PDF]
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability	Preprint	2019	[24]	[PDF]
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning	ICDM	2020	[109]	[PDF]
A3: Activation Anomaly Analysis	ECML-PKDD	2020	[88]	[PDF], [Code]

4.13. Interactive Outlier Detection

Paper Title	Venue	Year	Ref	Materials
Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback	SDM	2019	[45]	[PDF]
Interactive anomaly detection on attributed networks	WSDM	2019	[26]	[PDF]
eX2: a framework for interactive anomaly detection	IUI Workshop	2019	[7]	[PDF]
Tripartite Active Learning for Interactive Anomaly Discovery	IEEE Access	2019	[117]	[PDF]

4.14. Outlier Detection in Other fields

Field	Paper Title	Venue	Year	Ref	Materials
Text	Outlier detection for text data	SDM	2017	[40]	[PDF]

4.15. Outlier Detection Applications

Field	Paper Title	Venue	Year	Ref	Materials
Security	A survey of distance and similarity measures used within network intrusion anomaly detection	IEEE Commun. Surv. Tutor.	2015	[97]	[PDF]
Security	Anomaly-based network intrusion detection: Techniques, systems and challenges	Computers & Security	2009	[32]	[PDF]
Finance	A survey of anomaly detection techniques in financial domain	Future Gener Comput Syst	2016	[3]	[PDF]
Traffic	Outlier Detection in Urban Traffic Data	WIMS	2018	[27]	[PDF]
Social Media	A survey on social media anomaly detection	SIGKDD Explorations	2016	[107]	[PDF]
Social Media	GLAD: group anomaly detection in social media analysis	TKDD	2015	[106]	[PDF]
Machine Failure	Detecting the Onset of Machine Failure Using Anomaly Detection Methods	DAWAK	2019	[80]	[PDF]
Video Surveillance	AnomalyNet: An anomaly detection network for video surveillance	TIFS	2019	[116]	[IEEE], Code

4.16. Automated Outlier Detection

Paper Title	Venue	Year	Ref	Materials
AutoML: state of the art with a focus on anomaly detection, challenges, and research directions	Int J Data Sci Anal	2022	[8]	[PDF]
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning	ICDE	2020	[59]	[PDF]
Automatic Unsupervised Outlier Model Selection	NeurIPS	2021	[113]	[PDF], [Code]

4.17. Machine Learning Systems for Outlier Detection

This section summarizes a list of systems for outlier detection, which may overlap with the section of tools and libraries.

Paper Title	Venue	Year	Ref	Materials
PyOD: A Python Toolbox for Scalable Outlier Detection	JMLR	2019	[112]	[PDF], [Code]
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection	MLSys	2021	[114]	[PDF], [Code]
TOD: Tensor-based Outlier Detection	Preprint	2021	[115]	[PDF], [Code]

4.18. Fairness and Bias in Outlier Detection

Paper Title	Venue	Year	Ref	Materials
A Framework for Determining the Fairness of Outlier Detection	ECAI	2020	[25]	[PDF]
FAIROD: Fairness-aware Outlier Detection	AIES	2021	[86]	[PDF]

4.19. Isolation-Based Methods

Paper Title	Venue	Year	Ref	Materials
Isolation forest	ICDM	2008	[55]	[PDF]
Isolation‐based anomaly detection using nearest‐neighbor ensembles	Computational Intelligence	2018	[9]	[PDF], [Code]
Extended Isolation Forest	TKDE	2019	[35]	[PDF], [Code]
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection	KDD	2020	[93]	[PDF], [Code]
Deep Isolation Forest for Anomaly Detection	TKDE	2023	[99]	[PDF], [Code]

4.20. Emerging and Interesting Topics

Paper Title	Venue	Year	Ref	Materials
Clustering with Outlier Removal	TKDE	2019	[56]	[PDF]
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning	IEEE Trans. Ind. Informat.	2020	[17]	[PDF]
SSD: A Unified Framework for Self-Supervised Outlier Detection	ICLR	2021	[85]	[PDF], [Code]

5. Key Conferences/Workshops/Journals

5.1. Conferences & Workshops

Key data mining conference deadlines, historical acceptance rates, and more can be found data-mining-conferences.

ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD). Note: SIGKDD usually has an Outlier Detection Workshop (ODD), see ODD 2021.

ACM International Conference on Management of Data (SIGMOD)

The Web Conference (WWW)

IEEE International Conference on Data Mining (ICDM)

SIAM International Conference on Data Mining (SDM)

IEEE International Conference on Data Engineering (ICDE)

ACM InternationalConference on Information and Knowledge Management (CIKM)

ACM International Conference on Web Search and Data Mining (WSDM)

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

5.2. Journals

ACM Transactions on Knowledge Discovery from Data (TKDD)

IEEE Transactions on Knowledge and Data Engineering (TKDE)

ACM SIGKDD Explorations Newsletter

Data Mining and Knowledge Discovery

Knowledge and Information Systems (KAIS)

References

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[10]	Bhatia, S., Hooi, B., Yoon, M., Shin, K. and Faloutsos. C., 2020. MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams. In AAAI Conference on Artificial Intelligence (AAAI).

[11]	Braei, M. and Wagner, S., 2020. Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv preprint arXiv:2004.00433.

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[16]	Campos, G.O., Moreira, E., Meira Jr, W. and Zimek, A., 2019. Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models. Computer Science & Information Systems, 16(2).

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[19]	Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. ACM computing surveys , 41(3), p.15.

[20]	Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. Tutorial at ICDM 2011.

[21]	Chen, J., Sathe, S., Aggarwal, C. and Turaga, D., 2017, June. Outlier detection with autoencoder ensembles. SIAM International Conference on Data Mining, pp. 90-98. Society for Industrial and Applied Mathematics.

[22]	Dai, E. and Chen, J., 2022. Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series. International Conference on Learning Representations (ICLR).

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[24]	Das, S., Islam, M.R., Jayakodi, N.K. and Doppa, J.R., 2019. Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability. arXiv preprint arXiv:1901.08930.

[25]	Davidson, I. and Ravi, S.S., 2020. A framework for determining the fairness of outlier detection. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI2020) (Vol. 2029).

[26]	Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357-365. ACM.

[27]	Djenouri, Y. and Zimek, A., 2018, June. Outlier detection in urban traffic data. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. ACM.

[28]	Domingues, R., Filippone, M., Michiardi, P. and Zouaoui, J., 2018. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, pp.406-421.

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