Summary of Graph Learning in the Era Of Llms: a Survey From the Perspective Of Data, Models, and Tasks, by Xunkai Li et al.
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
by Xunkai Li, Zhengyu Wu, Jiayi Wu, Hanwen Cui, Jishuo Jia, Rong-Hua Li, Guoren Wang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)
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 The paper explores the integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) to leverage graph description texts with rich semantic context, enhancing data quality and improving model-centric approaches. The authors combine GNNs’ structural relationship capture with LLMs’ contextual understanding to address a wide range of Text-Attributed Graph (TAG) tasks in various domains. This integrated approach enables cross-domain generalization, allowing a single graph model to handle diverse downstream tasks. The work serves as a foundational reference for advancing graph learning methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two powerful AI models to better understand complex graphs. By using language models to learn from text descriptions of graphs, and combining that with the ability of graph neural networks to understand relationships between nodes, researchers can create more accurate and versatile models for tasks like graph learning, reasoning, and question answering. This is especially useful in real-world scenarios where data comes from different sources and has varying levels of complexity. |
Keywords
» Artificial intelligence » Domain generalization » Question answering
