Graph Domain Adaptation: Challenges, Progress and Prospects

• URL: http://arxiv.org/abs/2402.00904v1
• Date: Thu, 1 Feb 2024 02:44:32 GMT
• Title: Graph Domain Adaptation: Challenges, Progress and Prospects
• Authors: Boshen Shi, Yongqing Wang, Fangda Guo, Bingbing Xu, Huawei Shen, Xueqi Cheng
• Abstract summary: We propose graph domain adaptation as an effective knowledge-transfer paradigm across graphs. GDA introduces a bunch of task-related graphs as source graphs and adapts the knowledge learnt from source graphs to the target graphs. We outline the research status and challenges, propose a taxonomy, introduce the details of representative works, and discuss the prospects.
• Score: 61.9048172631524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As graph representation learning often suffers from label scarcity problems in real-world applications, researchers have proposed graph domain adaptation (GDA) as an effective knowledge-transfer paradigm across graphs. 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 research status and challenges, propose a taxonomy, introduce the details of representative works, and discuss the prospects. To the best of our knowledge, this paper is the first survey for graph domain adaptation. A detailed paper list is available at https://github.com/Skyorca/Awesome-Graph-Domain-Adaptation-Papers.

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