22 citations · 24 across the 4 of their papers we have counts for
6 papers
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…
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning
Gaichao Li, Jinsong Chen, John E. Hopcroft +1
Graph pooling methods have been widely used on downsampling graphs, achieving impressive results on multiple graph-level tasks like graph classification and graph generation. An im…
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…
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
Jinsong Chen, Chang Liu, Kaiyuan Gao +2
Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs. To thi…
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning
Gaichao Li, Jinsong Chen, Kun He
By incorporating the graph structural information into Transformers, graph Transformers have exhibited promising performance for graph representation learning in recent years. Exis…
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
Jinsong Chen, Kaiyuan Gao, Gaichao Li +1
The graph Transformer emerges as a new architecture and has shown superior performance on various graph mining tasks. In this work, we observe that existing graph Transformers trea…