Learning on Graphs has attracted immense attention due to its wide real-world
applications. The most popular pipeline for learning on graphs with textual
node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes
shallow text embedding as initial node representations, which has limitations
in general knowledge and profound semantic understanding. In recent years,
Large Language Models (LLMs) have been proven to possess extensive common
knowledge and powerful semantic comprehension abilities that have
revolutionized existing workflows to handle text data. In this paper, we aim to
explore the potential of LLMs in graph machine learning, especially the node
classification task, and investigate two possible pipelines: LLMs-as-Enhancers
and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text
attributes with their massive knowledge and then generate predictions through
GNNs. The latter attempts to directly employ LLMs as standalone predictors. We
conduct comprehensive and systematical studies on these two pipelines under
various settings. From comprehensive empirical results, we make original
observations and find new insights that open new possibilities and suggest
promising directions to leverage LLMs for learning on graphs.

本文旨在探索使用大型语言模型（LLMs）进行图机器学习的潜力，特别是节点分类任务，并调查两种潜在的流程：LLMs 作为增强器和 LLMs 作为预测器。通过全面而系统的研究，我们获得了原创性观察并发现新的见解，为利用 LLMs 进行图学习提供了新的可能性和有希望的方向。