3 citations · 3 across the 5 of their papers we have counts for
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cs.NE2024
Learning Graph Quantized Tokenizers
Limei Wang, Kaveh Hassani, Si Zhang +7
Transformers serve as the backbone architectures of Foundational Models, where domain-specific tokenizers allow them to adapt to various domains. Graph Transformers (GTs) have rece…
cs.CL2024
How to Make LMs Strong Node Classifiers?
Zhe Xu, Kaveh Hassani, Si Zhang +7
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tas…
cs.LG2024★ 3 cited
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Dongqi Fu, Zhigang Hua, Yan Xie +7
Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex…