  Graphs play an important role in representing complex relationships in
various domains like social networks, knowledge graphs, and molecular
discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have
emerged as a cornerstone in Graph Machine Learning (Graph ML), facilitating the
representation and processing of graph structures. Recently, LLMs have
demonstrated unprecedented capabilities in language tasks and are widely
adopted in a variety of applications such as computer vision and recommender
systems. This remarkable success has also attracted interest in applying LLMs
to the graph domain. Increasing efforts have been made to explore the potential
of LLMs in advancing Graph ML's generalization, transferability, and few-shot
learning ability. Meanwhile, graphs, especially knowledge graphs, are rich in
reliable factual knowledge, which can be utilized to enhance the reasoning
capabilities of LLMs and potentially alleviate their limitations such as
hallucinations and the lack of explainability. Given the rapid progress of this
research direction, a systematic review summarizing the latest advancements for
Graph ML in the era of LLMs is necessary to provide an in-depth understanding
to researchers and practitioners. Therefore, in this survey, we first review
the recent developments in Graph ML. We then explore how LLMs can be utilized
to enhance the quality of graph features, alleviate the reliance on labeled
data, and address challenges such as graph heterogeneity and
out-of-distribution (OOD) generalization. Afterward, we delve into how graphs
can enhance LLMs, highlighting their abilities to enhance LLM pre-training and
inference. Furthermore, we investigate various applications and discuss the
potential future directions in this promising field.
