Domain Adaptation for Graph Representation Learning: Challenges, Progress, and Prospects

doi:10.1007/s11390-024-4465-x

	Domain Adaptation for Graph Representation Learning: Challenges, Progress, and Prospects
	Shi, Bo-Shen 1,2; Wang, Yong-Qing 1; Guo, Fang-Da 1; Xu, Bing-Bing 1; Shen, Hua-Wei 1,2; Cheng, Xue-Qi 1,2
	2025-03-28
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
页码	18
摘要	Graph representation learning often faces knowledge scarcity in real-world applications, including limited labels and sparse relationships. Although a range of methods have been proposed to address these problems, such as graph few-shot learning, they mainly rely on inadequate knowledge within the task graph, which would limit their effectiveness. Moreover, they fail to consider other potentially useful task-related graphs. To overcome these limitations, domain adaptation for graph representation learning has emerged as an effective paradigm for transferring knowledge across graphs. It is also recognized as graph domain adaptation (GDA). In particular, to enhance model performance on target graphs with specific tasks, GDA introduces a bunch of task-related graphs as source graphs and adapts the knowledge learnt from source graphs to the target graphs. Since GDA combines the advantages of graph representation learning and domain adaptation, it has become a promising direction of transfer learning on graphs and has attracted an increasing amount of research interest in recent years. In this paper, we comprehensively overview the studies of GDA and present a detailed survey of recent advances. Specifically, we outline the current research status, analyze key challenges, propose a taxonomy, introduce representative work and practical applications, and discuss future prospects. To the best of our knowledge, this paper is the first survey for graph domain adaptation.
关键词	graph domain adaptation graph representation learning transfer learning
DOI	10.1007/s11390-024-4465-x
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB0680302] ; National Key Research and Development Program of China[2023YFC3305303] ; National Natural Science Foundation of China[62372434] ; National Natural Science Foundation of China[62302485] ; China Postdoctoral Science Foundation[2022M713206] ; CAS Special Research Assistant Program ; Key Research Project of Chinese Academy of Sciences[RCJJ-145-2-21]
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:001455769000001
出版者	SPRINGER SINGAPORE PTE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40665
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Yong-Qing
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab AI Safety, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shi, Bo-Shen,Wang, Yong-Qing,Guo, Fang-Da,et al. Domain Adaptation for Graph Representation Learning: Challenges, Progress, and Prospects[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2025:18.
APA	Shi, Bo-Shen,Wang, Yong-Qing,Guo, Fang-Da,Xu, Bing-Bing,Shen, Hua-Wei,&Cheng, Xue-Qi.(2025).Domain Adaptation for Graph Representation Learning: Challenges, Progress, and Prospects.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,18.
MLA	Shi, Bo-Shen,et al."Domain Adaptation for Graph Representation Learning: Challenges, Progress, and Prospects".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2025):18.