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

Understand

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.