PyGOD: A Python Library for Graph Outlier Detection

  • Kay Liu
  • , Yingtong Dou
  • , Xueying Ding
  • , Xiyang Hu
  • , Ruitong Zhang
  • , Hao Peng
  • , Lichao Sun
  • , Philip S. Yu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalJournal of Machine Learning Research
Volume25
StatePublished - 2024
Externally publishedYes

Keywords

  • anomaly detection
  • graph learning
  • graph neural networks
  • outlier detection

Fingerprint

Dive into the research topics of 'PyGOD: A Python Library for Graph Outlier Detection'. Together they form a unique fingerprint.

Cite this