Graph plays an important role in representing complex relationships in
real-world applications such as social networks, biological data and citation
networks. In recent years, Large Language Models (LLMs) have achieved
tremendous success in various domains, which makes applying LLMs to graphs
particularly appealing. However, directly applying LLMs to graph modalities
presents unique challenges due to the discrepancy and mismatch between the
graph and text modalities. Hence, to further investigate LLMs' potential for
comprehending graph information, we introduce GraphPrompter, a novel framework
designed to align graph information with LLMs via soft prompts. Specifically,
GraphPrompter consists of two main components: a graph neural network to encode
complex graph information and an LLM that effectively processes textual
information. Comprehensive experiments on various benchmark datasets under node
classification and link prediction tasks demonstrate the effectiveness of our
proposed method. The GraphPrompter framework unveils the substantial
capabilities of LLMs as predictors in graph-related tasks, enabling researchers
to utilize LLMs across a spectrum of real-world graph scenarios more
effectively.

通过 GraphPrompter 框架，将 Large Language Models（LLMs）与图形模态对齐，实现了 LLMs 在图相关任务中的重要性和效果。