2 citations · 2 across the 4 of their papers we have counts for
4 papers
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
Rui Xue, Tong Zhao, Neil Shah +1
Graph neural networks (GNNs) have demonstrated remarkable success in graph representation learning, and various sampling approaches have been proposed to scale GNNs to applications…
Linear-Time Graph Neural Networks for Scalable Recommendations
Jiahao Zhang, Rui Xue, Wenqi Fan +4
In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' futu…
Efficient End-to-end Language Model Fine-tuning on Graphs
Rui Xue, Xipeng Shen, Ruozhou Yu +1
Learning from Text-Attributed Graphs (TAGs) has attracted significant attention due to its wide range of real-world applications. The rapid evolution of language models (LMs) has r…
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Rui Xue, Haoyu Han, MohamadAli Torkamani +2
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting sca…