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
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…
cs.AI2025
Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift
Shixuan Liu, Yue He, Yunfei Wang +5
Logical rule learning, a prominent category of knowledge graph (KG) reasoning methods, constitutes a critical research area aimed at learning explicit rules from observed facts to…