用大模型提升文本图的泛化能力，解决长邻居和嵌入不匹配问题。
Model Generalization on Text Attribute Graphs: Principles with Large Language Models
- 用任务自适应提示统一属性空间，增强文本嵌入泛化性
- 通过大模型估计参数的信念传播，实现跨图通用的信息聚合
- 在11个真实数据集上显著领先，适合图学习与大模型结合研究者
大语言模型（LLMs）被引入图学习领域，旨在将零样本泛化优势扩展至标签数据稀缺的任务。在文本属性图（TAGs）推理中，现有方法面临两大挑战：LLM上下文长度限制导致难以处理大节点邻域，以及节点嵌入与LLM词元空间不匹配。为此，本文提出两个关键原则并构建框架LLM-BP：(1) 通过基于LLM的编码器和任务感知提示，统一属性空间以增强文本属性嵌入的泛化能力；(2) 设计可泛化的图信息聚合机制，采用由LLM估计参数的信念传播，实现跨图自适应。在11个真实世界TAG基准上的评估显示，使用任务条件嵌入使性能提升8.10%，进一步采用自适应聚合再增1.71%。代码与任务自适应嵌入已开源。
原文摘要 · Abstract (English)
Large language models (LLMs) have recently been introduced to graph learning, aiming to extend their zero-shot generalization success to tasks where labeled graph data is scarce. Among these applications, inference over text-attributed graphs (TAGs) presents unique challenges: existing methods struggle with LLMs' limited context length for processing large node neighborhoods and the misalignment between node embeddings and the LLM token space. To address these issues, we establish two key principles for ensuring generalization and derive the framework LLM-BP accordingly: (1) Unifying the attribute space with task-adaptive embeddings, where we leverage LLM-based encoders and task-aware prompting to enhance generalization of the text attribute embeddings; (2) Developing a generalizable graph information aggregation mechanism, for which we adopt belief propagation with LLM-estimated parameters that adapt across graphs. Evaluations on 11 real-world TAG benchmarks demonstrate that LLM-BP significantly outperforms existing approaches, achieving 8.10% improvement with task-conditional embeddings and an additional 1.71% gain from adaptive aggregation. The code and task-adaptive embeddings are publicly available.
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