.png){kind=icon}
.png){kind=icon}
Return
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TBDATA.2025.3624955.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Tokenized graph Transformers have advanced node classification by transforming graphs into token sequences, but existing methods suffer from limited flexibility due to single-type token generation, which captures partial graph information and requires tailored modifications. To address this, we propose NTFormer, a novel graph Transformer with a dedicated token generator called Node2Par. Node2Par constructs diverse token sequences for each node using multiple token elements (i.e., neighborhood tokens and node tokens) from both topology view and attribute view, enabling comprehensive expression of graph features from multi-perspectives. Leveraging the outputs of Node2Pars, NTFormer adopts a standard Transformer backbone without additional graph-aware modules and a learnable information fusion strategy to adaptively learn expressive node representations from generated different token sequences, eliminating the need for tailored encoding strategies. Extensive experiments on benchmark datasets including homophily and heterophily graphs showcase that NTFormer outperforms representative graph Transformers and GNNs in node classification.
Keywords:
Graph transformer
token generator
node classification
Node2Par
Journal
I
IF:
5.7
Papers:
834
Citations:
3.0K