PyGDA：一個用於圖域適應的Python函式庫

PyGDA: A Python Library for Graph Domain Adaptation

March 13, 2025

作者: Zhen Zhang, Meihan Liu, Bingsheng He

cs.AI

摘要

圖域適應已成為促進跨領域知識轉移的一種前景廣闊的方法。近年來，眾多模型被提出以增強該領域的泛化能力。然而，目前尚無統一庫將現有技術整合並簡化其實現。為填補這一空白，我們推出了PyGDA，一個專為圖域適應設計的開源Python庫。作為該領域首個全面庫，PyGDA涵蓋了20多種廣泛使用的圖域適應方法及多種類型的圖數據集。具體而言，PyGDA提供了模塊化組件，使用戶能夠利用多種常用工具函數無縫構建自定義模型。為處理大規模圖數據，PyGDA支持採樣和小批量處理等特性，確保計算效率。此外，PyGDA還包含全面的性能基準測試及詳盡的用戶友好API，方便研究人員和實踐者使用。為促進便捷訪問，PyGDA以MIT許可證發布於https://github.com/pygda-team/pygda，API文檔則位於https://pygda.readthedocs.io/en/stable/。

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/.