2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Towards Scalable and Deep Graph Neural Networks via Noise Masking
Yuxuan Liang, Wentao Zhang, Zeang Sheng +5
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks. However, scaling them to large graphs is challenging due to the high comp…
cs.LG2024
Retrofitting Temporal Graph Neural Networks with Transformer
Qiang Huang, Xiao Yan, Xin Wang +5
Temporal graph neural networks (TGNNs) outperform regular GNNs by incorporating time information into graph-based operations. However, TGNNs adopt specialized models (e.g., TGN, TG…
cs.LG2023★ 2 cited
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
Yuxiang Wang, Xiao Yan, Chuang Hu +5
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learn…