  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.
