ekth0r / pygod

A Python Library for Graph Outlier Detection (Anomaly Detection)

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PyGOD is a Python library for graph outlier detection (anomaly detection). This exciting yet challenging field has many key applications, e.g., detecting suspicious activities in social networks [1] and security systems [2].

PyGOD includes more than 10 latest graph-based detection algorithms, such as DOMINANT (SDM'19) and GUIDE (BigData'21). For consistently and accessibility, PyGOD is developed on top of PyTorch Geometric (PyG) and PyTorch, and follows the API design of PyOD. See examples below for detecting outliers with PyGOD in 5 lines!

PyGOD is under actively developed and will be updated frequently! Please star, watch, and fork.

PyGOD is featured for:

• Unified APIs, detailed documentation, and interactive examples across various graph-based algorithms.
• Comprehensive coverage of more than 10 latest graph outlier detectors.
• Full support of detections at multiple levels, such as node-, edge-, and graph-level tasks (WIP).
• Streamline data processing with PyG--fully compatible with PyG data objects.

Outlier Detection Using PyGOD with 5 Lines of Code:

# train a dominant detector
from pygod.models import DOMINANT

model = DOMINANT()  # hyperparameters can be set here
model.fit(data)  # data is a Pytorch Geometric data object

# get outlier scores on the input data
outlier_scores = model.decision_scores # raw outlier scores on the input data

# predict on the new data
outlier_scores = model.decision_function(test_data) # raw outlier scores on the input data  # predict raw outlier scores on test

Citing PyGOD (to be announced soon):

PyGOD paper will be available on arxiv soon. If you use PyGOD in a scientific publication, we would appreciate citations to the following paper (to be announced):

@article{tba,
  author  = {tba},
  title   = {PyGOD: A Comprehensive Python Library for Graph Outlier Detection},
  journal = {tba},
  year    = {2022},
}

or:

tba, 2022. PyGOD: A Comprehensive Python Library for Graph Outlier Detection. tba.

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Installation

It is recommended to use pip or conda (wip) for installation. Please make sure the latest version is installed, as PyGOD is updated frequently:

pip install pygod            # normal install
pip install --upgrade pygod  # or update if needed

Alternatively, you could clone and run setup.py file:

git clone https://github.com/pygod-team/pygod.git
cd pygod
pip install .

Required Dependencies:

• Python 3.6 +
• argparse>=1.4.0
• numpy>=1.19.4
• scikit-learn>=0.22.1
• scipy>=1.5.2
• pandas>=1.1.3
• setuptools>=50.3.1.post20201107

Note on PyG and PyTorch Installation: PyGOD depends on PyTorch Geometric (PyG), PyTorch, and networkx. To streamline the installation, PyGOD does NOT install these libraries for you. Please install them from the above links for running PyGOD:

• torch>=1.10
• pytorch_geometric>=2.0.3
• networkx>=2.6.3

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API Cheatsheet & Reference

Full API Reference: (https://docs.pygod.org). API cheatsheet for all detectors:

• fit(X): Fit detector.
• decision_function(G): Predict raw anomaly score of PyG data G using the fitted detector.
• predict(G): Predict if nodes in PyG data G is an outlier or not using the fitted detector.
• predict_proba(G): Predict the probability of nodes in PyG data G being outlier using the fitted detector.
• predict_confidence(G): Predict the model's node-wise confidence (available in predict and predict_proba) [3].

Key Attributes of a fitted model:

• decision_scores_: The outlier scores of the training data. The higher, the more abnormal. Outliers tend to have higher scores.
• labels_: The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies.

Implemented Algorithms

PyGOD toolkit consists of two major functional groups:

(i) Node-level detection :

Type	Backbone	Abbr	Algorithm	Year	Ref
Unsupervised	MLP	MLPAE	Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction	2014	[4]
Unsupervised	GNN	GCNAE	Variational Graph Auto-Encoders	2016	[5]
Unsupervised	MF	ONE	Outlier aware network embedding for attributed networks	2019	[6]
Unsupervised	GNN	DOMINANT	Deep anomaly detection on attributed networks	2019	[7]
Unsupervised	GNN	DONE	Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding	2020	[8]
Unsupervised	GNN	AdONE	Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding	2020	[8]
Unsupervised	GNN	AnomalyDAE	AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks	2020	[9]
Unsupervised	GAN	GAAN	Generative Adversarial Attributed Network Anomaly Detection	2020	[10]
Unsupervised	GNN	OCGNN	One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks	2021	[11]
Unsupervised	GNN	GUIDE	Higher-order Structure Based Anomaly Detection on Attributed Networks	2021	[12]

(ii) Utility functions :

Type	Name	Function	Documentation
Metric	eval_precision_at_k	Calculating Precision@k	eval_precision_at_k
Metric	eval_recall_at_k	Calculating Recall@k	eval_recall_at_k
Metric	eval_roc_auc	Calculating ROC-AUC Score	eval_roc_auc
Data	gen_structure_outliers	Generating structural outliers	gen_structure_outliers
Data	gen_attribute_outliers	Generating attribute outliers	gen_attribute_outliers

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Quick Start for Outlier Detection with PyGOD

"A Blitz Introduction" demonstrates the basic API of PyGOD using the dominant detector. It is noted that the API across all other algorithms are consistent/similar.

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How to Contribute

You are welcome to contribute to this exciting project:

See contribution guide for more information.

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PyGOD Team

PyGOD is a great team effort by researchers from UIC, IIT, BUAA, ASU, and CMU. Our core team members include:

Kay Liu (UIC), Yingtong Dou (UIC), Yue Zhao (CMU), Xueying Ding (CMU), Xiyang Hu (CMU), Ruitong Zhang (BUAA), Kaize Ding (ASU), Canyu Chen (IIT),

Reach out us by submitting an issue report or send an email to [email protected].

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Reference

[1]	Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H. and Yu, P.S., 2020, October. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM).

[2]	Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D. and Chen, H., 2021, October. Structural temporal graph neural networks for anomaly detection in dynamic graphs. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM).

[3]	Perini, L., Vercruyssen, V., Davis, J. Quantifying the confidence of anomaly detectors in their example-wise predictions. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 2020.

[4]	Sakurada, M. and Yairi, T., 2014, December. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis.

[5]	Kipf, T.N. and Welling, M., 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308.

[6]	Bandyopadhyay, S., Lokesh, N. and Murty, M.N., 2019, July. Outlier aware network embedding for attributed networks. In Proceedings of the AAAI conference on artificial intelligence (AAAI).

[7]	Ding, K., Li, J., Bhanushali, R. and Liu, H., 2019, May. Deep anomaly detection on attributed networks. In Proceedings of the SIAM International Conference on Data Mining (SDM).

[8]	(1, 2) Bandyopadhyay, S., Vivek, S.V. and Murty, M.N., 2020, January. Outlier resistant unsupervised deep architectures for attributed network embedding. In Proceedings of the International Conference on Web Search and Data Mining (WSDM).

[9]	Fan, H., Zhang, F. and Li, Z., 2020, May. AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]	Chen, Z., Liu, B., Wang, M., Dai, P., Lv, J. and Bo, L., 2020, October. Generative adversarial attributed network anomaly detection. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM).

[11]	Wang, X., Jin, B., Du, Y., Cui, P., Tan, Y. and Yang, Y., 2021. One-class graph neural networks for anomaly detection in attributed networks. Neural computing and applications.

[12]	Yuan, X., Zhou, N., Yu, S., Huang, H., Chen, Z. and Xia, F., 2021, December. Higher-order Structure Based Anomaly Detection on Attributed Networks. In 2021 IEEE International Conference on Big Data (Big Data).