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.

翻译：图在表示社交网络、知识图谱和分子发现等各个领域的复杂关系中发挥着重要作用。随着深度学习的出现，图神经网络（GNNs）已成为图机器学习（Graph ML）的基石，促进了图结构的表示与处理。近期，大语言模型（LLMs）在语言任务中展现出前所未有的能力，并被广泛应用于计算机视觉和推荐系统等多种应用场景。这一显著成功也引发了将LLMs应用于图领域的兴趣。越来越多的研究致力于探索LLMs在提升图机器学习泛化能力、迁移能力和小样本学习能力方面的潜力。与此同时，图（尤其是知识图谱）蕴含丰富的可靠事实知识，可用于增强LLMs的推理能力，并可能缓解其幻觉和缺乏可解释性等局限性。鉴于这一研究方向的快速发展，有必要通过系统综述总结LLMs时代图机器学习的最新进展，以帮助研究人员和从业者深入理解。因此，本综述首先回顾图机器学习的最新发展，然后探讨如何利用LLMs提升图特征质量、减轻对标注数据的依赖，并解决图异质性和分布外（OOD）泛化等挑战。随后，我们深入分析图如何增强LLMs，强调其在提升LLM预训练和推理能力方面的作用。最后，我们探讨了各种应用，并讨论这一前景广阔领域的未来潜在发展方向。