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.

翻译：图在表示现实世界应用（如社交网络、生物数据和引文网络）中的复杂关系方面发挥着重要作用。近年来，大语言模型（LLMs）在各个领域取得了巨大成功，这使得将LLMs应用于图任务极具吸引力。然而，由于图模态与文本模态之间的差异和不匹配，直接应用LLMs处理图模态面临独特挑战。因此，为了进一步探究LLMs理解图信息的潜力，我们提出GraphPrompter——一种通过软提示将图信息与LLMs对齐的新型框架。具体来说，GraphPrompter包含两个主要组件：用于编码复杂图信息的图神经网络，以及有效处理文本信息的LLM。在节点分类和链路预测任务上，基于多种基准数据集的全面实验验证了我们提出方法的有效性。GraphPrompter框架揭示了LLMs作为图相关任务预测器的强大能力，使研究人员能够更有效地利用LLMs处理各种真实世界图场景。