3 papers
cs.LG2026
Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment
Chunyu Hu, Tianyin Liao, Ge Lan +4
The paper introduces GTAlign, a simple framework that aligns graph structures to tabular representations, enabling a text-free Graph Foundation Model that uses community-guided con…
cs.LG2026
Invariant Graph Transformer for Out-of-Distribution Generalization
Tianyin Liao, Ziwei Zhang, Yufei Sun +2
Graph Transformers (GTs) have demonstrated great effectiveness across various graph analytical tasks. However, the existing GTs focus on training and testing graph data originated…
cs.LG2026
TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Models
Tianyin Liao, Chunyu Hu, Yicheng Sui +4
Link prediction is a fundamental task in graph machine learning with widespread applications such as recommendation systems, drug discovery, knowledge graphs, etc. In the foundatio…