Summary of Large Language Models Meet Graph Neural Networks: a Perspective Of Graph Mining, by Yuxin You et al.
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
by Yuxin You, Zhen Liu, Xiangchao Wen, Yongtao Zhang, Wei Ai
First submitted to arxiv on: 26 Dec 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 The paper explores the intersection of Large Language Models (LLMs) and Graph Neural Networks (GNNs) in graph mining, aiming to address the limitations of GNNs in generalizing to diverse graph data. The review presents a novel taxonomy for research in this field, dividing it into three main categories: GNN-driving-LLM, LLM-driving-GNN, and GNN-LLM-co-driving. It reveals the capabilities of LLMs in enhancing graph feature extraction and improving downstream tasks such as node classification, link prediction, and community detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about combining two powerful tools in computer science: Large Language Models (LLMs) and Graph Neural Networks (GNNs). They help us understand graphs better. The authors group their ideas into three main parts: GNN-led LLMs, LLM-led GNNs, and working together. This helps them see what each one does well and how they can work together to make graph analysis better. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Gnn
