Summary of Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge From Large Language Models, By Quan Li et al.
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
by Quan Li, Tianxiang Zhao, Lingwei Chen, Junjie Xu, Suhang Wang
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers address the limitations of graph neural networks (GNNs) in few-shot node classification tasks, a common challenge in real-world applications. Conventional GNNs struggle when there are only a few labeled nodes, despite their ability to excel in node classification. The authors propose an innovative approach that combines Large Language Models (LLMs) and GNNs, leveraging the zero-shot inference capabilities of LLMs and employing a Graph-LLM-based active learning paradigm to enhance GNNs’ performance. This novel method demonstrates significant improvements in node classification accuracy with limited labeled data, surpassing state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are great at classifying nodes on graphs, but they struggle when there aren’t many labeled nodes. That’s a big problem because many real-world applications have few labeled nodes. Researchers want to make GNNs better in these situations. They came up with an idea that combines two powerful tools: Large Language Models (LLMs) and GNNs. This new method uses the LLMs’ ability to figure things out without being taught, and then it helps the GNNs learn even more from the limited labeled data. This leads to better node classification accuracy and leaves old methods behind. |
Keywords
» Artificial intelligence » Active learning » Classification » Few shot » Inference » Zero shot
