论文标题
图形数据的数据增强：最新进步
Data Augmentation for Graph Data: Recent Advancements
论文作者
论文摘要
基于图形神经网络（GNN）方法最近已成为处理图数据的流行工具，因为它们能够合并结构信息。 GNNS性能的唯一障碍是缺乏标记的数据。图像和文本数据的数据增强技术无法用于图形数据，因为图形数据的复杂和非欧几里得结构。这一差距迫使研究人员将注意力转向开发图形数据的数据增强技术。大多数提出的图形数据增强（GDA）技术都是特定于任务的。在本文中，我们根据不同的图形任务调查了现有的GDA技术。这项调查不仅提供了GDA研究界的参考，而且还向其他领域的研究人员提供了必要的信息。
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.
