A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges Article Swipe
Xuansheng Wu
,
Kaixiong Zhou
,
Mingchen Sun
,
Xin Wang
,
Ninghao Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.07275
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.07275
The recent "pre-train, prompt, predict training" paradigm has gained popularity as a way to learn generalizable models with limited labeled data. The approach involves using a pre-trained model and a prompting function that applies a template to input samples, adding indicative context and reformulating target tasks as the pre-training task. However, the design of prompts could be a challenging and time-consuming process in complex tasks. The limitation can be addressed by using graph data, as graphs serve as structured knowledge repositories by explicitly modeling the interaction between entities. In this survey, we review prompting methods from the graph perspective, where prompting functions are augmented with graph knowledge. In particular, we introduce the basic concepts of graph prompt learning, organize the existing work of designing graph prompting functions, and describe their applications and future challenges. This survey will bridge the gap between graphs and prompt design to facilitate future methodology development.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.07275
- https://arxiv.org/pdf/2303.07275
- OA Status
- green
- Cited By
- 9
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4324299457Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2303.07275Digital Object Identifier
- Title
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A Survey of Graph Prompting Methods: Techniques, Applications, and ChallengesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-13Full publication date if available
- Authors
-
Xuansheng Wu, Kaixiong Zhou, Mingchen Sun, Xin Wang, Ninghao LiuList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.07275Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2303.07275Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2303.07275Direct OA link when available
- Concepts
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Computer science, Popularity, Graph, Data science, Human–computer interaction, Theoretical computer science, Artificial intelligence, Social psychology, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 5, 2023: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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