As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adversarial attacks. Recent efforts have been made on tackling these issues from a modeling perspective which requires additional cost of changing model architectures or re-training model parameters. In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance. We provide theoretical analysis on the design of the framework and discuss why adapting graph data works better than adapting the model. Extensive experiments have demonstrated the effectiveness of GTrans on three distinct scenarios for eight benchmark datasets where suboptimal data is presented. Remarkably, GTrans performs the best in most cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on three experimental settings. Code is released at https://github.com/ChandlerBang/GTrans.

翻译：图神经网络作为图表示学习的强大工具，已推动从药物发现到推荐系统等众多应用的发展。然而，数据质量问题（如分布偏移、异常特征和对抗攻击）严重挑战了图神经网络的效能。近期研究尝试从建模角度解决这些问题，但需要额外成本修改模型架构或重新训练模型参数。本文提供了一种数据中心的解决方案，提出名为GTrans的图变换框架，该框架在测试时自适应优化图数据以提升性能。我们对框架设计进行了理论分析，并论证了为何优化图数据比优化模型更为有效。在包含非最优数据的八个基准数据集上，针对三种不同场景的大量实验证明了GTrans的有效性。值得注意的是，在三种实验设置下，GTrans在多数情况下性能最优，相较于最佳基线分别提升2.8%、8.2%和3.8%。代码已开源：https://github.com/ChandlerBang/GTrans。