用大模型提升图神经网络可信度，系统梳理融合路径与方向
Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy
- 构建四类融合方法的分类体系，厘清技术路线差异
- 揭示大模型如何增强图神经网络的语义理解与可解释性
- 适合关注AI可信性与多模态融合的研究者参考
随着图神经网络（GNNs）在多个领域的广泛应用，其可信度问题已成为研究热点。已有研究表明，将大语言模型（LLMs）与GNNs结合，可提升GNNs的语义理解与生成能力，从而从多个维度增强其可信性。本文提出一种系统性分类框架，帮助研究者清晰理解不同方法的原理与应用场景，并梳理了四类代表性融合策略。通过该分类体系，研究人员可把握各类方法的适用场景、潜在优势与局限性。最后，本文展望了未来在提升模型可信度方面，大模型与图神经网络融合的前沿方向与发展趋势。
原文摘要 · Abstract (English)
With the extensive application of Graph Neural Networks (GNNs) across various domains, their trustworthiness has emerged as a focal point of research. Some existing studies have shown that the integration of large language models (LLMs) can improve the semantic understanding and generation capabilities of GNNs, which in turn improves the trustworthiness of GNNs from various aspects. Our review introduces a taxonomy that offers researchers a clear framework for comprehending the principles and applications of different methods and helps clarify the connections and differences among various approaches. Then we systematically survey representative approaches along the four categories of our taxonomy. Through our taxonomy, researchers can understand the applicable scenarios, potential advantages, and limitations of each approach for the the trusted integration of GNNs with LLMs. Finally, we present some promising directions of work and future trends for the integration of LLMs and GNNs to improve model trustworthiness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。