Data augmentation has recently seen increased interest in graph machine learning given its demonstrated ability to improve model performance and generalization by added training data. Despite this recent surge, the area is still relatively under-explored, due to the challenges brought by complex, non-Euclidean structure of graph data, which limits the direct analogizing of traditional augmentation operations on other types of image, video or text data. Our work aims to give a necessary and timely overview of existing graph data augmentation methods; notably, we present a comprehensive and systematic survey of graph data augmentation approaches, summarizing the literature in a structured manner. We first introduce three different taxonomies for categorizing graph data augmentation methods from the data, task, and learning perspectives, respectively. Next, we introduce recent advances in graph data augmentation, differentiated by their methodologies and applications. We conclude by outlining currently unsolved challenges and directions for future research. Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain. Additionally, we provide a continuously updated reading list at https://github.com/zhao-tong/graph-data-augmentation-papers.

翻译：最近,由于显示有能力改进模型性能和通过增加培训数据加以概括化,数据扩增最近对图表机学习的兴趣有所增加。尽管最近出现了这种激增,但由于图表数据复杂、非欧化结构带来的挑战,该地区仍然相对没有得到充分探讨,因为图形数据结构复杂、非欧化结构带来挑战,限制了对其他类型的图像、视频或文本数据进行传统扩增操作的直接模拟,从而限制了对其他类型的图像、视频或文本数据进行传统扩增操作的直接模拟。我们的工作旨在对现有图表数据扩增方法进行必要和及时的概览;特别是,我们展示了对图表数据扩增方法的全面和系统调查,以结构化的方式对文献进行总结。我们首先从数据、任务和学习角度分别引入了三种不同的图表数据扩增方法分类。接下来,我们介绍了图表数据扩增方面的最新进展,按其方法和应用加以区分。我们最后通过概述目前尚未解决的挑战和未来研究的方向。总体而言,我们的工作旨在澄清图表数据扩增扩增现有文献的概况,并激励这一领域的额外工作,为研究人员和从业人员提供较广的图形机学习领域的有用资源。我们提供了在https://githubbub/go-tamamentalmentalmentalation.commamentalation.comtament-talogmentmentmentment.tament-tamentalation.tagatation.comtagata-tamentalmentalmentalations.