  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.
