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A Survey of Graph Meets Large Language Model: Progress and Future Directions

IJCAI 2024(2024)

The Hong Kong University of Science and Technology (Guangzhou | The Chinese University | Tsinghua University

Cited 91|Views170
Abstract
Graph plays a significant role in representing and analyzing complexrelationships in real-world applications such as citation networks, socialnetworks, and biological data. Recently, Large Language Models (LLMs), whichhave achieved tremendous success in various domains, have also been leveragedin graph-related tasks to surpass traditional Graph Neural Networks (GNNs)based methods and yield state-of-the-art performance. In this survey, we firstpresent a comprehensive review and analysis of existing methods that integrateLLMs with graphs. First of all, we propose a new taxonomy, which organizesexisting methods into three categories based on the role (i.e., enhancer,predictor, and alignment component) played by LLMs in graph-related tasks. Thenwe systematically survey the representative methods along the three categoriesof the taxonomy. Finally, we discuss the remaining limitations of existingstudies and highlight promising avenues for future research. The relevantpapers are summarized and will be consistently updated at:https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.
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Key words
Data Mining: DM: Mining graphs,Natural Language Processing: NLP: Applications
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