图半监督蒸馏新方法被KDD2026录用，利用文本属性提升图表示学习效率。
arXiv:2607.20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph top…
扩散语言模型跨界图学习，文本属性图建模迎来新范式，值得关注的技术突破。
arXiv:2606.31166v1 Announce Type: cross Abstract: Text-attributed graphs (TAGs), where each node carries a natural language description, require model…
把图上万条邻居文本压成精华提示，让大模型在图推理上更准更省。
arXiv:2601.08187v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising capabilities in Text-Attributed Graph (TA…
大模型在文本属性图上实现零样本泛化，GraspLLM方法带来全新突破。
arXiv:2606.11898v1 Announce Type: cross Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad…
聚焦能量对齐方法，为GNN与LLM在文本图上的协同建模拓展新思路。
arXiv:2606.10461v1 Announce Type: cross Abstract: Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe …
重新审视LLM如何利用拓扑与文本双重信息，提升文本属性图的分布外检测性能，被KDD 2026收录的研究。
arXiv:2602.11641v2 Announce Type: replace Abstract: Text-attributed graphs (TAGs) associate nodes with textual attributes and graph structure, enablin…
提出针对稀疏文本属性图的高效可迁移预训练方法S2Aligner，解决LLM对齐中的监督不足问题。
arXiv:2605.18579v1 Announce Type: new Abstract: Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation mod…