3 papers
cs.AI2026
Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning
Dongxiao He, Jiayu Zhang, Jitao Zhao +2
Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing methods for multi-label node…
cs.LG2026
Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models
Yi Wang, Jitao Zhao, Di Jin +1
Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for enabling knowledge transfer across diverse domains. Unlike traditional graph learning methods that…
cs.LG2026
What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models
Dongxiao He, Siqi Liu, Jitao Zhao +3
Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for general-purpose graph learning, aiming to learn reusable knowledge that generalizes across diverse…