Summary of Towards Graph Prompt Learning: a Survey and Beyond, by Qingqing Long et al.
Towards Graph Prompt Learning: A Survey and Beyond
by Qingqing Long, Yuchen Yan, Peiyan Zhang, Chen Fang, Wentao Cui, Zhiyuan Ning, Meng Xiao, Ning Cao, Xiao Luo, Lingjun Xu, Shiyue Jiang, Zheng Fang, Chong Chen, Xian-Sheng Hua, Yuanchun Zhou
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A large-scale “pre-train and prompt learning” approach has been successful in various domains such as question answering, image recognition, and multimodal retrieval. This paradigm leverages pre-trained models to reduce data requirements and computational costs while enhancing model applicability across tasks. However, this success has not translated well to graph-structured data, where node and edge features have disparate distributions and topological structures differ significantly. To alleviate these disparities, we need to explore prompt design methodologies, compare related techniques, assess application scenarios and datasets, identify unresolved problems and challenges, and categorize relevant works in the field. This survey summarizes general design principles and latest applications, including text-attributed graphs, molecules, proteins, and recommendation systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large-scale “pre-train and prompt learning” paradigms are very good at doing many things, like answering questions and recognizing images. But they’re not as good when it comes to working with special kinds of data called graph-structured data. Graphs are like maps that show connections between things. The problem is that these graphs have different patterns and structures than other types of data. To fix this, researchers need to find better ways to design prompts for graph data and compare different approaches. This review looks at over 100 papers on this topic and summarizes the latest ideas and applications. |
Keywords
» Artificial intelligence » Prompt » Question answering
