Summary of Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects, by Pengfei Ding and Yan Wang and Guanfeng Liu
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects
by Pengfei Ding, Yan Wang, Guanfeng Liu
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reviews few-shot learning on heterogeneous graphs (FLHG), a topic gaining attention due to label sparsity issues in prevailing studies. Despite recent efforts proposing various methods and applications, there is a need for a comprehensive overview of existing approaches. This review categorizes FLHG methods into single-heterogeneity, dual-heterogeneity, and multi-heterogeneity types, analyzing the research progress within each category. The paper highlights recent developments and identifies promising directions for future research in FLHG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Few-shot learning on heterogeneous graphs is a big deal! Right now, scientists are working to make it better by reviewing what they’ve already done. They’re grouping these ideas into three types: single-heterogeneity, dual-heterogeneity, and multi-heterogeneity. This helps us see what’s been accomplished so far and where we should go next. |
Keywords
* Artificial intelligence * Attention * Few shot
