随着人工智能技术的飞速发展，图神经网络(Graph Neural Networks，GNN)在处理图数据方面展现出卓越的性能，而大语言模型(Large Language Model，LLM)在自然语言处理领域也取得了显著成就。文章旨在探索GNN与LLM的融合策略，以增强模型对复杂场景的理解与处理能力。文章分析了图结构的特点和GNN的工作原理，介绍了LLM的核心架构和预训练策略。在此基础上，文章提出了多种融合策略，包括将LLM作为特征增强器、结构编码器、预测生成器、多模态对齐器和知识融合器，阐述了实现这些角色的具体技术，如特征嵌入融合、跨模态注意力机制、联合训练框架等。通过融合策略，模型不仅能够整合图的结构特征与文本的语义信息，还能够有效处理跨模态数据，提升模型的泛化能力，在推荐系统、知识图谱和生物信息学等领域展现出显著的应用价值。文章认为，这种融合策略对于提升人工智能处理复杂数据和实现通用智能的重要性不容忽视。
Research on fusion of graph neural network and large language model
With the rapid development of artificial intelligence technology,Graph Neural Networks(GNN)have shown outstanding performance in handling graph data,while Large Language Models(LLM)have also made significant achievements in natural language processing.This paper aims to explore the fusion strategies of GNN and LLM to enhance the model's understanding and processing capabilities of complex scenarios.The characteristics of graph-structured data and the working principles of GNN are analyzed,followed by an introduction to the core architecture and pre-training strategies of LLM.Based on this,various fusion strategies are proposed,including using LLM as feature enhancers,structure encoders,prediction generators,multi-modal aligners,and knowledge integrators.The specific techniques for implementing these roles,such as feature embedding fusion,cross-modal attention mechanisms,and joint training frameworks,are detailed.Through these fusion strategies,the model can integrate both the structural features of graphs and the semantic information of text,effectively handle cross-modal data,enhance model generalization,and demonstrate significant application value in fields such as recommendation systems,knowledge graphs,and bio-informatics.This paper emphasizes the importance of such fusion strategies in improving artificial intelligence's ability to process complex data and achieve general intelligence.
graph neural networklarge language modelgraph structurefusion strategy
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