  Graph learning is a prevalent domain that endeavors to learn the intricate
relationships among nodes and the topological structure of graphs. Over the
years, graph learning has transcended from graph theory to graph data mining.
With the advent of representation learning, it has attained remarkable
performance in diverse scenarios. Owing to its extensive application prospects,
graph learning attracts copious attention. While some researchers have
accomplished impressive surveys on graph learning, they failed to connect
related objectives, methods, and applications in a more coherent way. As a
result, they did not encompass current ample scenarios and challenging problems
due to the rapid expansion of graph learning. Particularly, large language
models have recently had a disruptive effect on human life, but they also show
relative weakness in structured scenarios. The question of how to make these
models more powerful with graph learning remains open. Our survey focuses on
the most recent advancements in integrating graph learning with pre-trained
language models, specifically emphasizing their application within the domain
of large language models. Different from previous surveys on graph learning, we
provide a holistic review that analyzes current works from the perspective of
graph structure, and discusses the latest applications, trends, and challenges
in graph learning. Specifically, we commence by proposing a taxonomy and then
summarize the methods employed in graph learning. We then provide a detailed
elucidation of mainstream applications. Finally, we propose future directions.

Artificial Intelligence