论文标题
图表上的数据增强：一项技术调查
Data Augmentation on Graphs: A Technical Survey
论文作者
论文摘要
近年来，图表表示学习在遭受低质量数据问题的同时取得了巨大的成功。作为提高计算机视觉数据质量的成熟技术，数据增强也引起了图形域的越来越多的关注。为了促进这一新兴方向的研究，这项调查提供了现有的图形数据增强（Gdaug）技术的全面审查和摘要。具体而言，这项调查首先概述了各种可行分类法，并根据多尺度图元素对现有的GDAUG研究进行了分类。随后，对于每种类型的Gdaug技术，本调查都对标准化的技术定义进行了形式化，讨论技术细节并提供示意图。该调查还回顾了特定于域的图数据增强技术，包括用于异质图，时间图，时空图和超图的技术。此外，这项调查还摘要有关图形数据增强的可用评估指标和设计指南。最后，它概述了Gdaug在数据和模型级别上的应用，讨论了该领域的开放问题，并期待将来的方向。 Gdaug的最新进展总结在Github中。
In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques. Specifically, this survey first provides an overview of various feasible taxonomies and categorizes existing GDAug studies based on multi-scale graph elements. Subsequently, for each type of GDAug technique, this survey formalizes standardized technical definition, discuss the technical details, and provide schematic illustration. The survey also reviews domain-specific graph data augmentation techniques, including those for heterogeneous graphs, temporal graphs, spatio-temporal graphs, and hypergraphs. In addition, this survey provides a summary of available evaluation metrics and design guidelines for graph data augmentation. Lastly, it outlines the applications of GDAug at both the data and model levels, discusses open issues in the field, and looks forward to future directions. The latest advances in GDAug are summarized in GitHub.
