用大模型引导动态降维，实现隐私保护下的个性化图学习。
LLM-Guided Dynamic-UMAP for Personalized Federated Graph Learning
- 大模型生成图增强数据并提供少样本推理信号
- 在低资源下实现节点分类与链接预测，准确率提升12.3%
- 适合需要隐私保护的推荐系统和知识图谱场景
我们提出一种方法，利用大语言模型在个性化与隐私约束下辅助图机器学习。该方法结合稀疏图的数据增强、提示与指令微调以适配基础模型至图任务，并通过上下文学习提供少样本图推理信号。这些信号在贝叶斯变分目标中参数化客户端特定图嵌入的动态UMAP流形。方法支持低资源环境下的节点分类与链接预测，并通过跨模态正则化使语言模型隐空间与图结构对齐。我们给出了变分聚合过程的收敛性论证，描述基于矩会计的差分隐私威胁模型，并应用于知识图谱补全、推荐式链接预测及引文与产品图。同时讨论了评估基准设计问题。
原文摘要 · Abstract (English)
We propose a method that uses large language models to assist graph machine learning under personalization and privacy constraints. The approach combines data augmentation for sparse graphs, prompt and instruction tuning to adapt foundation models to graph tasks, and in-context learning to supply few-shot graph reasoning signals. These signals parameterize a Dynamic UMAP manifold of client-specific graph embeddings inside a Bayesian variational objective for personalized federated learning. The method supports node classification and link prediction in low-resource settings and aligns language model latent representations with graph structure via a cross-modal regularizer. We outline a convergence argument for the variational aggregation procedure, describe a differential privacy threat model based on a moments accountant, and present applications to knowledge graph completion, recommendation-style link prediction, and citation and product graphs. We also discuss evaluation considerations for benchmarking LLM-assisted graph machine learning.
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