OpenGDA

OpenGDA是由数据智能系统研究中心创建的综合性图域适应基准，旨在评估跨网络学习任务中的图域适应模型。该数据集包含多种任务类型，如节点、边和图级别的分类，涵盖了网络信息系统、城市系统和自然系统等多个场景。数据集经过预处理和标准化，便于研究者使用。OpenGDA的应用领域广泛，主要用于解决图数据中的标签和结构知识转移问题，提升模型在实际应用中的性能。

OpenGDA is a comprehensive graph domain adaptation benchmark developed by the Data Intelligence System Research Center, aiming to evaluate graph domain adaptation models for cross-network learning tasks. This dataset covers diverse task types including node-level, edge-level and graph-level classification, and spans multiple scenarios such as network information systems, urban systems and natural systems. It has been preprocessed and standardized to facilitate researchers' utilization. OpenGDA has broad application domains, and is primarily used to address the label and structural knowledge transfer issues in graph data so as to improve the performance of models in real-world applications.