The extraordinary performance of large language models has not only reshaped the research landscape in the field of NLP but has also demonstrated its exceptional applicative potential in various domains. However, the potential of these models in mining relationships from graph data remains under-explored. Graph neural networks, as a popular research area in recent years, have numerous studies on relationship mining. Yet, current cutting-edge research in graph neural networks has not been effectively integrated with large language models, leading to limited efficiency and capability in graph relationship mining tasks. A primary challenge is the inability of LLMs to deeply exploit the edge information in graphs, which is critical for understanding complex node relationships. This gap limits the potential of LLMs to extract meaningful insights from graph structures, limiting their applicability in more complex graph-based analysis. We focus on how to utilize existing LLMs for mining and understanding relationships in graph data, applying these techniques to recommendation tasks. We propose an innovative framework that combines the strong contextual representation capabilities of LLMs with the relationship extraction and analysis functions of GNNs for mining relationships in graph data. Specifically, we design a new prompt construction framework that integrates relational information of graph data into natural language expressions, aiding LLMs in more intuitively grasping the connectivity information within graph data. Additionally, we introduce graph relationship understanding and analysis functions into LLMs to enhance their focus on connectivity information in graph data. Our evaluation on real-world datasets demonstrates the framework's ability to understand connectivity information in graph data.

翻译：大语言模型卓越的性能不仅重塑了自然语言处理领域的研究格局，还展现出在多个领域中非凡的应用潜力。然而，这些模型在图数据结构中挖掘关系的能力尚未得到充分探索。图神经网络作为近年来的热门研究方向，已有大量关于关系挖掘的研究。但当前图神经网络的前沿研究尚未与大语言模型有效融合，导致图关系挖掘任务的效率与能力受限。主要挑战在于大语言模型无法深度利用图数据中的边信息——这对理解复杂节点关系至关重要。这一缺陷限制了大语言模型从图结构中提取有意义洞察的潜力，制约了其在更复杂的图分析任务中的应用。我们聚焦于如何利用现有大语言模型挖掘与理解图数据中的关系，并将这些技术应用于推荐任务。我们提出创新框架，将大语言模型强大的上下文表征能力与图神经网络的关系抽取分析功能相结合，用于挖掘图数据中的关系。具体而言，我们设计了一种新的提示构建框架，将图数据的关系信息融入自然语言表达，辅助大语言模型更直观地把握图数据的连通性信息。同时，我们在模型中引入图关系理解与分析功能，增强其对图数据连通性信息的关注。在真实数据集上的评估表明，该框架能够有效理解图数据中的连通性信息。