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…