首个图领域自适应开源库，简化模型实现与评估。
PyGDA: A Python Library for Graph Domain Adaptation
- 提供20+图领域自适应方法的模块化组件
- 支持采样与小批量处理，高效处理大规模图数据
- 适合研究者快速实验，也适合工程师落地应用
图领域自适应已成为跨域知识迁移的有前景方法。尽管已有众多模型提升其泛化能力，但尚无统一库整合现有技术并简化实现。为此，我们推出PyGDA——首个面向图领域自适应的开源Python库。PyGDA涵盖20余种常用图领域自适应方法及多种图数据集，提供模块化组件，支持用户灵活构建自定义模型。针对大规模图，集成采样与小批量处理功能，保障高效计算。同时提供全面性能基准与友好易用的API文档。PyGDA以MIT许可证发布于https://github.com/pygda-team/pygda，API文档见https://pygda.readthedocs.io/en/stable/。
原文摘要 · Abstract (English)
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/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。