5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2024
GQWformer: A Quantum-based Transformer for Graph Representation Learning
Lei Yu, Hongyang Chen, Jingsong Lv +1
Graph Transformers (GTs) have demonstrated significant advantages in graph representation learning through their global attention mechanisms. However, the self-attention mechanism…
cs.LG2023★ 5 cited
Learning Invariant Molecular Representation in Latent Discrete Space
Xiang Zhuang, Qiang Zhang, Keyan Ding +5
Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when dat…
cs.SI2023★ 1 cited
Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?
Jingsong Lv, Hongyang Chen, Yao Qi +1
In this paper, we introduce two local graph features for missing link prediction tasks on ogbl-citation2. We define the features as Circle Features, which are borrowed from the con…