2 papers
cs.CL2026
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
Zhongjian Zhang, Yue Yu, Mengmei Zhang +3
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a widely recognized paradigm, Gra…
cs.CV2025
Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework
Yujie Xing, Xiao Wang, Bin Wu +2
Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning due to their ability to model diverse node interactions. However, existing GTs often…