PyGOD: A Python Library for Graph Outlier Detection

Published in

Journal of machine learning research(2022)

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TL;DR

As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for outlier detection under an easy-to-use, well-documented API designed for use by both researchers and practitioners.

Abstract

PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for outlier detection under an easy-to-use, well-documented API designed for use by both researchers and practitioners. PyGOD provides modularized components of the different detectors implemented so that users can easily customize each detector for their purposes. To ease the construction of detection workflows, PyGOD offers numerous commonly used utility functions. To scale computation to large graphs, PyGOD supports functionalities for deep models such as sampling and mini-batch processing. PyGOD uses best practices in fostering code reliability and maintainability, including unit testing, continuous integration, and code coverage. To facilitate accessibility, PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI).

Authors

Yue Zhao

11 papers

Philip S. Yu

5 papers

Xiyang Hu

6 papers

References52 items

GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language

DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

TwiBot-22: Towards Graph-Based Twitter Bot Detection

PyGOD: A Python Library for Graph Outlier Detection

Published in

Journal of machine learning research(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Yue Zhao

11 papers

Philip S. Yu

5 papers

Xiyang Hu

6 papers

References52 items

GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language

DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

TwiBot-22: Towards Graph-Based Twitter Bot Detection

Yingtong Dou

5 papers

George H. Chen

3 papers

Xueying Ding

2 papers

Ruitong Zhang

3 papers

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

Higher-order Structure Based Anomaly Detection on Attributed Networks

TOD: GPU-accelerated Outlier Detection via Tensor Operations

Subtractive Aggregation for Attributed Network Anomaly Detection

A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Few-shot Network Anomaly Detection via Cross-network Meta-learning

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

Generative Adversarial Attributed Network Anomaly Detection

MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization

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Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description

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Inductive Anomaly Detection on Attributed Networks

Array programming with NumPy

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc

PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning

One-class graph neural networks for anomaly detection in attributed networks

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Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding

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Deep Anomaly Detection on Attributed Networks

Fast Graph Representation Learning with PyTorch Geometric

Interactive Anomaly Detection on Attributed Networks

PyOD: A Python Toolbox for Scalable Outlier Detection

Adversarial Attack and Defense on Graph Data: A Survey

Outlier Aware Network Embedding for Attributed Networks

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Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

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Variational Graph Auto-Encoders

Semi-Supervised Classification with Graph Convolutional Networks

Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction

Scikit-learn: Machine Learning in Python

Visual Evaluation of Outlier Detection Models

Exploring Network Structure, Dynamics, and Function using NetworkX

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Contrastive Attributed Network Anomaly Detection with Data Augmentation

Automatic Unsupervised Outlier Model Selection

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions

An Introduction to Outlier Analysis

Independent consultant

Interpreting and Unifying Outlier Scores

Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

Journal of machine learning research

Type

journal

URL

http://www.ai.mit.edu/projects/jmlr/

Alternate Names

Journal of Machine Learning Research
J mach learn res
J Mach Learn Res