2 papers
cs.LG2024
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning
Wenbin Hu, Huihao Jing, Qi Hu +2
Textual graphs are ubiquitous in real-world applications, featuring rich text information with complex relationships, which enables advanced research across various fields. Textual…
cs.LG2024
Gradformer: Graph Transformer with Exponential Decay
Chuang Liu, Zelin Yao, Yibing Zhan +3
Graph Transformers (GTs) have demonstrated their advantages across a wide range of tasks. However, the self-attention mechanism in GTs overlooks the graph's inductive biases, parti…