Graph Domain Adaptation: Challenges, Progress and Prospects
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929650060492800 |
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| author | Shi, Boshen Wang, Yongqing Guo, Fangda Xu, Bingbing Shen, Huawei Cheng, Xueqi |
| author_facet | Shi, Boshen Wang, Yongqing Guo, Fangda Xu, Bingbing Shen, Huawei Cheng, Xueqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00904 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Domain Adaptation: Challenges, Progress and Prospects Shi, Boshen Wang, Yongqing Guo, Fangda Xu, Bingbing Shen, Huawei Cheng, Xueqi Machine Learning Artificial Intelligence 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. |
| title | Graph Domain Adaptation: Challenges, Progress and Prospects |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.00904 |