Graph Neural Network (GNNs) based methods have recently become a popular tool to deal with graph data because of their ability to incorporate structural information. The only hurdle in the performance of GNNs is the lack of labeled data. Data Augmentation techniques for images and text data can not be used for graph data because of the complex and non-euclidean structure of graph data. This gap has forced researchers to shift their focus towards the development of data augmentation techniques for graph data. Most of the proposed Graph Data Augmentation (GDA) techniques are task-specific. In this paper, we survey the existing GDA techniques based on different graph tasks. This survey not only provides a reference to the research community of GDA but also provides the necessary information to the researchers of other domains.
翻译:以图表神经网络(GNN)为基础的方法最近成为处理图表数据的一个流行工具,因为它们有能力纳入结构信息,GNN工作的唯一障碍是缺乏标签数据。图像和文本数据的数据增强技术不能用于图表数据,因为图形数据结构复杂,非电子化。这一差距迫使研究人员将重点转向开发图表数据的数据增强技术。拟议的图表数据增强技术大多是特定任务。在本文件中,我们根据不同的图表任务调查现有的GDA技术。这项调查不仅为GDA的研究机构提供了参考,而且还为其他领域的研究人员提供了必要的信息。