Summary of Graph Machine Learning in the Era Of Large Language Models (llms), by Wenqi Fan et al.
Graph Machine Learning in the Era of Large Language Models (LLMs)
by Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Social and Information Networks (cs.SI)
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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 reviews the recent advancements in Graph Machine Learning (Graph ML) in the era of Large Language Models (LLMs). Graphs are crucial for representing complex relationships in domains like social networks, knowledge graphs, and molecular discovery. GNNs have been a cornerstone in Graph ML, enabling the representation and processing of graph structures. LLMs have achieved remarkable success in language tasks and are now being explored for their potential to enhance Graph ML’s generalization, transferability, and few-shot learning ability. The paper first reviews recent developments in Graph ML before exploring how LLMs can be utilized to improve graph features, alleviate reliance on labeled data, and address challenges like graph heterogeneity and out-of-distribution (OOD) generalization. It also delves into how graphs can enhance LLMs, highlighting their abilities to enhance pre-training and inference. The paper concludes by investigating various applications and discussing potential future directions in this promising field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at the latest advancements in using computers to understand complex relationships between things like people, ideas, or molecules. Right now, there are special computer models called Graph Neural Networks (GNNs) that help with this task. Recently, a new kind of computer model called Large Language Models (LLMs) has been very successful at understanding human language and is being explored to see if it can also improve our ability to understand complex relationships between things. The paper reviews what’s been happening in this area and explores how LLMs could be used to make computers better at understanding these relationships. It also talks about how using graphs, which are like maps of connections between things, can help computers become even more powerful. |
Keywords
» Artificial intelligence » Few shot » Generalization » Inference » Machine learning » Transferability
