Data-centric Graph Learning: A Survey
CoRR(2023)
摘要
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 three crucial questions:
(1) when to modify graph data, (2) what part of the graph data needs
modification to unlock the potential of various graph models, and (3) how to
safeguard graph models from problematic data influence. 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.
更多查看译文
关键词
graph,learning,data-centric
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要