pygod 0.2.0

A Python Library for Graph Outlier Detection (Anomaly Detection)

---

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 consistency 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 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- (WIP), and graph-level tasks (WIP).

• Scalable design for processing large graphs via mini-batch and sampling.

• 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(num_layers=4, epoch=20)  # 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 in the inductive setting
outlier_scores = model.decision_function(test_data) # raw outlier scores on the input data  # predict raw outlier scores on test

Citing PyGOD:

PyGOD paper is available on arxiv. If you use PyGOD in a scientific publication, we would appreciate citations to the following paper:

@article{pygod2022,
  author  = {Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Chen, George H. and Jia, Zhihao and Yu, Philip S.},
  title   = {PyGOD: A Python Library for Graph Outlier Detection},
  journal = {arXiv preprint arXiv:2204.12095},
  year    = {2022},
}

or:

Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K., Chen, G.H., Jia, Z., and Yu, P.S. 2022. PyGOD: A Python Library for Graph Outlier Detection. arXiv preprint arXiv:2204.12095.

---

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 +

• numpy>=1.19.4

• scikit-learn>=0.22.1

• scipy>=1.5.2

• 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

---

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.

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.

For the inductive setting:

• 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].

Input of PyGOD: Please pass in a PyTorch Geometric (PyG) data object. See PyG data processing examples.

Implemented Algorithms

PyGOD toolkit consists of two major functional groups:

(i) Node-level detection :

Type	Backbone	Abbr	Year	Sampling	Ref
Unsupervised	MLP	MLPAE	2014	Yes	[4]
Unsupervised	GNN	GCNAE	2016	Yes	[5]
Unsupervised	MF	ONE	2019	No	[6]
Unsupervised	GNN	DOMINANT	2019	Yes	[7]
Unsupervised	GNN	DONE	2020	Yes	[8]
Unsupervised	GNN	AdONE	2020	Yes	[8]
Unsupervised	GNN	AnomalyDAE	2020	Yes	[9]
Unsupervised	GAN	GAAN	2020	Yes	[10]
Unsupervised	GNN	OCGNN	2021	Yes	[11]
Unsupervised/SSL	GNN	CoLA (beta)	2021	In progress	[12]
Unsupervised/SSL	GNN	ANEMONE (beta)	2021	In progress	[13]
Unsupervised	GNN	GUIDE	2021	Yes	[14]
Unsupervised/SSL	GNN	CONAD	2022	Yes	[15]

(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
Metric	eval_average_precision	Calculating average precision	eval_average_precision
Data	gen_structure_outliers	Generating structural outliers	gen_structure_outliers
Data	gen_attribute_outliers	Generating attribute outliers	gen_attribute_outliers

---

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.

---

How to Contribute

You are welcome to contribute to this exciting project:

See contribution guide for more information.

---

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 [e-mail].

---

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]

Liu, Y., Li, Z., Pan, S., Gong, C., Zhou, C. and Karypis, G., 2021. Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE transactions on neural networks and learning systems (TNNLS).

[13]

Jin, M., Liu, Y., Zheng, Y., Chi, L., Li, Y. and Pan, S., 2021. ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM).

[14]

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).

[15]

Xu, Z., Huang, X., Zhao, Y., Dong, Y., and Li, J., 2022. Contrastive Attributed Network Anomaly Detection with Data Augmentation. In Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).