Balancing structure and position information in Graph Transformer network with a learnable node embedding

Thi Linh Hoang, Viet Cuong Ta

Abstract: Highlights•Graph Transformer Networks need both structural and positional encoding.•Propose a lightweight and robust node positional encoding.•Propose an adaptive learning for structural information.•Unify structure with positional information into a general node embedding.