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

• DOI: 10.24963/ijcai.2024/898
• Publication Date: 2024-07-26T14:28:11Z
• Authors: Yuhan Li; Zhixun Li; Peisong Wang; Jia Li; Xiangguo Sun; Hong Cheng; Jeffrey Xu Yu
• Keywords: Social and Information Networks (cs.SI); FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; 0202 electrical engineering, electronic engineering, information engineering; Computer Science - Social and Information Networks; 02 engineering and technology; Computation and Language (cs.CL); Machine Learning (cs.LG)

Graph plays a significant role in representing and analyzing complex relationships real-world applications such as citation networks, social biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success various domains, also been leveraged graph-related tasks to surpass traditional Neural Networks (GNNs) based methods yield state-of-the-art performance. In this survey, we first present comprehensive review analysis of existing that integrate LLMs with graphs. First all, propose new taxonomy, organizes into three categories on the (i.e., enhancer, predictor, alignment component) played by tasks. Then systematically survey representative along taxonomy. Finally, discuss remaining limitations studies highlight promising avenues for future research. The relevant papers are summarized will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.