A Survey of Graph Meets Large Language Model: Progress and Future Directions
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911850841505792 |
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| author | Li, Yuhan Li, Zhixun Wang, Peisong Li, Jia Sun, Xiangguo Cheng, Hong Yu, Jeffrey Xu |
| author_facet | Li, Yuhan Li, Zhixun Wang, Peisong Li, Jia Sun, Xiangguo Cheng, Hong Yu, Jeffrey Xu |
| contents | Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods and yield state-of-the-art performance. In this survey, we first present a comprehensive review and analysis of existing methods that integrate LLMs with graphs. First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. Then we systematically survey the representative methods along the three categories of the taxonomy. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. The relevant papers are summarized and will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12399 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey of Graph Meets Large Language Model: Progress and Future Directions Li, Yuhan Li, Zhixun Wang, Peisong Li, Jia Sun, Xiangguo Cheng, Hong Yu, Jeffrey Xu Machine Learning Computation and Language Social and Information Networks Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods and yield state-of-the-art performance. In this survey, we first present a comprehensive review and analysis of existing methods that integrate LLMs with graphs. First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. Then we systematically survey the representative methods along the three categories of the taxonomy. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. The relevant papers are summarized and will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks. |
| title | A Survey of Graph Meets Large Language Model: Progress and Future Directions |
| topic | Machine Learning Computation and Language Social and Information Networks |
| url | https://arxiv.org/abs/2311.12399 |