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 [DLS+20] and security systems [CCL+21].

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 anomalies 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.

Implemented Algorithms#

PyGOD toolkit consists of two major functional groups:

(i) Node-level detection :

Type	Backbone	Abbr	Algorithm	Year	Class
Unsupervised	GNN	DOMINANT	Deep anomaly detection on attributed networks	2019	`pygod.models.dominant.DOMINANT`
Unsupervised	GNN	AnomalyDAE	AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks	2020	`pygod.models.anomalydae.AnomalyDAE`
Unsupervised	GNN	DONE	Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding	2020	`pygod.models.done.DONE`
Unsupervised	GNN	AdONE	Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding	2020	`pygod.models.adone.AdONE`
Unsupervised	GNN	GCNAE	Variational Graph Auto-Encoders	2021	`pygod.models.gcnae.GCNAE`
Unsupervised	NN	MLPAE	Neural Networks and Deep Learning	2021	`pygod.models.mlpae.MLPAE`
Unsupervised	GNN	GUIDE	Higher-order Structure Based Anomaly Detection on Attributed Networks	2021	`pygod.models.guide.GUIDE`
Unsupervised	GNN	OCGNN	One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks	2021	`pygod.models.ocgnn.OCGNN`
Unsupervised	MF	ONE	Outlier aware network embedding for attributed networks	2019	`pygod.models.one.ONE`
Unsupervised	GAN	GAAN	Generative Adversarial Attributed Network Anomaly Detection	2020	`pygod.models.gaan.GAAN`

(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

API CheatSheet#

The following APIs are applicable for all detector models for easy use.

pygod.models.base.BaseDetector.fit(): Fit detector. y is ignored in unsupervised methods.
pygod.models.base.BaseDetector.decision_function(): Predict raw anomaly scores of PyG Graph G using the fitted detector
pygod.models.base.BaseDetector.predict(): Predict if a particular sample is an outlier or not using the fitted detector.
pygod.models.base.BaseDetector.predict_proba(): Predict the probability of a sample being outlier using the fitted detector.
pygod.models.base.BaseDetector.predict_confidence(): Predict the model’s sample-wise confidence (available in predict and predict_proba).
pygod.models.base.BaseDetector.process_graph() (you do not need to call this explicitly): Process the raw PyG data object into a tuple of sub data objects needed for the underlying model.

Key Attributes of a fitted model:

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

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