The history of artificial intelligence (AI) has witnessed the significant
impact of high-quality data on various deep learning models, such as ImageNet
for AlexNet and ResNet. Recently, instead of designing more complex neural
architectures as model-centric approaches, the attention of AI community has
shifted to data-centric ones, which focuses on better processing data to
strengthen the ability of neural models. Graph learning, which operates on
ubiquitous topological data, also plays an important role in the era of deep
learning. In this survey, we comprehensively review graph learning approaches
from the data-centric perspective, and aim to answer two crucial questions: (1)
when to modify graph data and (2) how to modify graph data to unlock the
potential of various graph models. Accordingly, we propose a novel taxonomy
based on the stages in the graph learning pipeline, and highlight the
processing methods for different data structures in the graph data, i.e.,
topology, feature and label. Furthermore, we analyze some potential problems
embedded in graph data and discuss how to solve them in a data-centric manner.
Finally, we provide some promising future directions for data-centric graph
learning.

人工智能发展历史中，高质量数据对各种深度学习模型的影响显著。最近，与基于模型的方法相比，AI 社区的关注重点转向了基于数据的方法，即更好地处理数据以增强神经模型的能力。这项调查从数据为中心的角度全面回顾了图学习方法，并旨在回答两个关键问题：（1）何时修改图数据和（2）如何修改图数据以发掘各种图模型的潜力。我们提出了一个基于图学习流程阶段的新分类法，并强调了处理图数据中不同数据结构（如拓扑、特征和标签）的方法。此外，我们分析了嵌入图数据中的一些潜在问题，并讨论了如何以数据为中心的方式来解决它们。最后，我们提供了一些关于数据为中心的图学习未来发展方向的前景。