Summary of A Survey Of Large Language Models For Graphs, by Xubin Ren et al.
A Survey of Large Language Models for Graphs
by Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh Chawla, Chao Huang
First submitted to arxiv on: 10 May 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 The paper presents a comprehensive survey of Large Language Models (LLMs) applied in graph learning tasks, highlighting the strengths and limitations of different frameworks. The authors detail four unique designs: GNNs as Prefix, LLMs as Prefix, LLMs-Graphs Integration, and LLMs-Only, emphasizing key methodologies within each category. The paper emphasizes potential avenues for future research, including overcoming integration challenges between LLMs and graph learning techniques, and venturing into new application areas. The authors maintain related open-source materials at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to use Large Language Models (LLMs) in graph learning tasks like link prediction and node classification. Right now, these models are really good at language comprehension and summarization. People want to combine LLMs with graph learning techniques to make them even better. The authors of this survey looked at the latest state-of-the-art LLMs applied in graph learning and came up with a new way to categorize existing methods based on their framework design. They talked about four different designs and what makes each one special. |
Keywords
» Artificial intelligence » Classification » Summarization
