title: PyGDA: A Python Library for Graph Domain Adaptation
summary: Graph domain adaptation has emerged as a promising approach to facilitate
  knowledge transfer across different domains. Recently, numerous models have
  been proposed to enhance their generalization capabilities in this field.
  However, there is still no unified library that brings together existing
  techniques and simplifies their implementation. To fill this gap, we introduce
  PyGDA, an open-source Python library tailored for graph domain adaptation. As
  the first comprehensive library in this area, PyGDA covers more than 20 widely
  used graph domain adaptation methods together with different types of graph
  datasets. Specifically, PyGDA offers modular components, enabling users to
  seamlessly build custom models with a variety of commonly used utility
  functions. To handle large-scale graphs, PyGDA includes support for features
  such as sampling and mini-batch processing, ensuring efficient computation. In
  addition, PyGDA also includes comprehensive performance benchmarks and
  well-documented user-friendly API for both researchers and practitioners. To
  foster convenient accessibility, PyGDA is released under the MIT license at
  https://github.com/pygda-team/pygda, and the API documentation is
  https://pygda.readthedocs.io/en/stable/.
paper_authors._id: 67d6471368b6209d9ad9f7f2
paper_authors.name: Zhen Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:12:18.394Z
paper_authors._id: 67d645ef9b5e492b00e79af9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/AW_0U1377safcLfIIwgA5.png
paper_authors.fullname: Zhen Zhang
paper_authors.type: user
paper_authors.user: zhenzhangcs
paper_authors._id: 67d6471368b6209d9ad9f7f3
paper_authors.name: Meihan Liu
paper_authors._id: 67d6471368b6209d9ad9f7f4
paper_authors.name: Bingsheng He
paper_title: PyGDA: A Python Library for Graph Domain Adaptation
paper_summary: Graph domain adaptation has emerged as a promising approach to facilitate
  knowledge transfer across different domains. Recently, numerous models have
  been proposed to enhance their generalization capabilities in this field.
  However, there is still no unified library that brings together existing
  techniques and simplifies their implementation. To fill this gap, we introduce
  PyGDA, an open-source Python library tailored for graph domain adaptation. As
  the first comprehensive library in this area, PyGDA covers more than 20 widely
  used graph domain adaptation methods together with different types of graph
  datasets. Specifically, PyGDA offers modular components, enabling users to
  seamlessly build custom models with a variety of commonly used utility
  functions. To handle large-scale graphs, PyGDA includes support for features
  such as sampling and mini-batch processing, ensuring efficient computation. In
  addition, PyGDA also includes comprehensive performance benchmarks and
  well-documented user-friendly API for both researchers and practitioners. To
  foster convenient accessibility, PyGDA is released under the MIT license at
  https://github.com/pygda-team/pygda, and the API documentation is
  https://pygda.readthedocs.io/en/stable/.
paper_ai_summary: PyGDA is an open-source library for graph domain adaptation that includes over 20 methods and supports large-scale graph processing efficiently.
paper_ai_keywords: graph domain adaptation PyGDA graph datasets sampling mini-batch processing performance benchmarks API documentation