针对任意图结构学习表示的通用框架，原创性高，已被ICML 2026接收。
arXiv:2512.11561v2 Announce Type: replace Abstract: Generalizing pretrained models to unseen datasets without retraining is a central challenge toward…
跳出传统图思维，用超图语言表达复杂关系，LLM建模迎来新范式。
arXiv:2605.21858v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong potential in modeling relational structures. H…
在图的分数傅里叶变换域中嵌入，突破传统谱嵌入的表达瓶颈，捕获更全面的图结构特征
arXiv:2508.02383v2 Announce Type: replace Abstract: Spectral graph embedding plays a critical role in graph representation learning by generating low-…