2 citations · 2 across the 2 of their papers we have counts for
5 papers · 1 filter
DAM-GT: Dual Positional Encoding-Based Attention Masking Graph Transformer for Node Classification
Chenyang Li, Jinsong Chen, John E. Hopcroft +1
Neighborhood-aware tokenized graph Transformers have recently shown great potential for node classification tasks. Despite their effectiveness, our in-depth analysis of neighborhoo…
Rethinking Tokenized Graph Transformers for Node Classification
Jinsong Chen, Chenyang Li, GaiChao Li +2
Node tokenized graph Transformers (GTs) have shown promising performance in node classification. The generation of token sequences is the key module in existing tokenized GTs which…
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification
Jinsong Chen, Siyu Jiang, Kun He
Recently, the emerging graph Transformers have made significant advancements for node classification on graphs. In most graph Transformers, a crucial step involves transforming the…
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
Jinsong Chen, Hanpeng Liu, John E. Hopcroft +1
While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for co…
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
Jinsong Chen, Gaichao Li, John E. Hopcroft +1
The emerging graph Transformers have achieved impressive performance for graph representation learning over graph neural networks (GNNs). In this work, we regard the self-attention…