22 citations · 25 across the 6 of their papers we have counts for
7 papers
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…
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification
Jinsong Chen, Boyu Li, Kun He
The decoupled Graph Convolutional Network (GCN), a recent development of GCN that decouples the neighborhood aggregation and feature transformation in each convolutional layer, has…
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks
Jinsong Chen, Boyu Li, Qiuting He +1
Node classification on attributed networks is a semi-supervised task that is crucial for network analysis. By decoupling two critical operations in Graph Convolutional Networks (GC…
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…