SPR: Toward a Graph Foundation Model for Transferable Graph Cognition via Spectral Patterns and Relational Geometry
Recently, Graph Foundation Models (GFMs) have attracted increasing attention for their potential to learn unified and generalizable knowledge across diverse graphs, thereby supporting a wide range of graph scenarios. However, unlike natural language and images, graphs lack an intuitive and unified form for perceiving and organizing transferable knowledge. Therefore, despite many initial explorations, a key problem remains unresolved: the transferable cognitive mechanism for graphs. Existing GFMs usually rely on intuitively defined mechanisms to encode transferable graph patterns, such as handcrafted structural templates (e.g., cycles and trees), fixed propagation mechanisms, and predefine...
| Authors | Ankang Yang, Jitao Zhao, Dongxiao He et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 June 2026 |
| Last updated at source | 26 September 2026 |
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