Pygod

A Python Library for Graph Outlier Detection (Anomaly Detection)

AI & Machine LearningPythonBSD-2-Clause

Abstract

Pygod is an open-source AI & Machine Learning project. 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 [#Dou2020Enhancing]_ and security systems [#Cai2021Structural]_. It is built using Python, Machine Learning, PyTorch. The complete source code is publicly available on GitHub under the BSD 2-Clause "Simplified" License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

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 [#Dou2020Enhancing]_ and security systems [#Cai2021Structural]_.

PyGOD includes 10+ graph outlier detection algorithms. 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!

# train a dominant detector from pygod.detector import DOMINANT

2. Objective

A Python Library for Graph Outlier Detection (Anomaly Detection)

This project demonstrates how Python, Machine Learning, PyTorch can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

PythonMachine LearningPyTorch

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

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

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by pygod-team and published on GitHub under the BSD 2-Clause "Simplified" License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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