Summary of Graphedit: Large Language Models For Graph Structure Learning, by Zirui Guo et al.
GraphEdit: Large Language Models for Graph Structure Learning
by Zirui Guo, Lianghao Xia, Yanhua Yu, Yuling Wang, Kangkang Lu, Zhiyong Huang, Chao Huang
First submitted to arxiv on: 23 Feb 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 This paper introduces GraphEdit, an innovative approach to learning graph structures by leveraging large language models (LLMs). Traditional methods rely on explicit graph structural information as supervision signals, which can be noisy or sparse. GraphEdit enhances the reasoning capabilities of LLMs through instruction-tuning over graph structures, allowing it to overcome these limitations and provide a comprehensive understanding of node-wise dependencies. The approach not only denoises connections but also identifies relationships from a global perspective. Experimental results on multiple benchmark datasets demonstrate the effectiveness and robustness of GraphEdit across various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to understand complex patterns in data that looks like networks or webs. This paper shows how to do just that by using special computer models called language models to learn about these patterns. The problem is that current methods rely on having extra information about the network’s structure, which can be noisy or missing. GraphEdit solves this problem by teaching the language model to understand the patterns in the data itself. This means it can denoise errors and find relationships between different parts of the network. The paper shows that GraphEdit works well across many different types of data. |
Keywords
* Artificial intelligence * Instruction tuning * Language model
