Data-centric AI, with its primary focus on the collection, management, and
utilization of data to drive AI models and applications, has attracted
increasing attention in recent years. In this article, we conduct an in-depth
and comprehensive review, offering a forward-looking outlook on the current
efforts in data-centric AI pertaining to graph data-the fundamental data
structure for representing and capturing intricate dependencies among massive
and diverse real-life entities. We introduce a systematic framework,
Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of
the graph data lifecycle, including graph data collection, exploration,
improvement, exploitation, and maintenance. A thorough taxonomy of each stage
is presented to answer three critical graph-centric questions: (1) how to
enhance graph data availability and quality; (2) how to learn from graph data
with limited-availability and low-quality; (3) how to build graph MLOps systems
from the graph data-centric view. Lastly, we pinpoint the future prospects of
the DC-GML domain, providing insights to navigate its advancements and
applications.

数据中心的人工智能 (Data-centric AI) 是近年来备受关注的研究领域，本文通过深入综述，展望了与图数据相关的数据中心的人工智能的当前努力，并介绍了系统性框架 Data-centric Graph Machine Learning (DC-GML)。最后，文章提出了 DC-GML 领域的未来前景，并提供了导航其进展和应用的见解。