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cs.LG2024
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…
cs.LG2023
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…
cs.LG2023★ 2 cited
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…