  Recently, the emerging graph Transformers have made significant advancements
for node classification on graphs. In most graph Transformers, a crucial step
involves transforming the input graph into token sequences as the model input,
enabling Transformer to effectively learn the node representations. However, we
observe that existing methods only express partial graph information of nodes
through single-type token generation. Consequently, they require tailored
strategies to encode additional graph-specific features into the Transformer to
ensure the quality of node representation learning, limiting the model
flexibility to handle diverse graphs. To this end, we propose a new graph
Transformer called NTFormer to address this issue. NTFormer introduces a novel
token generator called Node2Par, which constructs various token sequences using
different token elements for each node. This flexibility allows Node2Par to
generate valuable token sequences from different perspectives, ensuring
comprehensive expression of rich graph features. Benefiting from the merits of
Node2Par, NTFormer only leverages a Transformer-based backbone without
graph-specific modifications to learn node representations, eliminating the
need for graph-specific modifications. Extensive experiments conducted on
various benchmark datasets containing homophily and heterophily graphs with
different scales demonstrate the superiority of NTFormer over representative
graph Transformers and graph neural networks for node classification.
