PyGDA: A Python Library for Graph Domain Adaptation
Authors: name: Zhen Zhang, name: Meihan Liu, name: Bingsheng He

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/.