  Graphs are an essential data structure utilized to represent relationships in
real-world scenarios. Prior research has established that Graph Neural Networks
(GNNs) deliver impressive outcomes in graph-centric tasks, such as link
prediction and node classification. Despite these advancements, challenges like
data sparsity and limited generalization capabilities continue to persist.
Recently, Large Language Models (LLMs) have gained attention in natural
language processing. They excel in language comprehension and summarization.
Integrating LLMs with graph learning techniques has attracted interest as a way
to enhance performance in graph learning tasks. In this survey, we conduct an
in-depth review of the latest state-of-the-art LLMs applied in graph learning
and introduce a novel taxonomy to categorize existing methods based on their
framework design. We detail four unique designs: i) GNNs as Prefix, ii) LLMs as
Prefix, iii) LLMs-Graphs Integration, and iv) LLMs-Only, highlighting key
methodologies within each category. We explore the strengths and limitations of
each framework, and emphasize potential avenues for future research, including
overcoming current integration challenges between LLMs and graph learning
techniques, and venturing into new application areas. This survey aims to serve
as a valuable resource for researchers and practitioners eager to leverage
large language models in graph learning, and to inspire continued progress in
this dynamic field. We consistently maintain the related open-source materials
at \url{https://github.com/HKUDS/Awesome-LLM4Graph-Papers}.
