Summary of Prog: a Graph Prompt Learning Benchmark, by Chenyi Zi et al.
ProG: A Graph Prompt Learning Benchmark
by Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, Jia Li
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Artificial general intelligence on graphs has made significant progress across various applications. However, the traditional ‘Pre-train & Fine-tune’ paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot settings. Graph prompt learning emerges as a promising alternative, leveraging lightweight prompts to manipulate data and fill the task gap by reformulating downstream tasks to the pretext. This paper addresses critical challenges in unifying diverse graph prompt models, evaluating their quality, and improving their usability for practical comparisons and selection. To this end, the authors introduce a comprehensive benchmark for graph prompt learning that integrates six pre-training methods, five state-of-the-art graph prompt techniques, and evaluates them across 15 diverse datasets to assess performance, flexibility, and efficiency. The authors also present ‘ProG’, an open-source library that streamlines the execution of various graph prompt models, facilitating objective evaluations. Furthermore, they propose a unified framework that categorizes existing graph prompt methods into two main approaches: prompts as graphs and prompts as tokens. This framework enhances the applicability and comparison of graph prompt techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence on graphs to solve problems. The traditional way of doing this has some limitations, especially when working with complex or limited data. A new approach called graph prompt learning uses simple prompts to help machines learn from data more effectively. To make this approach better, the authors created a benchmark that tests different ways of using graph prompts and compares their performance. They also developed an open-source library that makes it easier to use these prompts. The authors hope that by making these prompts more accessible and comparable, they can help machines work better with graphs in various applications. |
Keywords
» Artificial intelligence » Few shot » Prompt
