What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models
SliGFM is proposed, a graph foundation model built upon topology-aware sliding-window feature encoding and generative reconstruction that enables a smoothness-aware transformer to capture transferable relational patterns among feature tokens within each node, while the generative reconstruction objective encourages preservation of the original feature information.