3 papers
cs.LG2026
E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems
Rui Xue, Shichao Zhu, Liang Qin +1
Graph Neural Networks (GNNs) have emerged as powerful tools for modeling graph-structured data and have been widely used in recommender systems, such as for capturing complex user-…
cs.LG2025
VISAGNN: Versatile Staleness-Aware Efficient Training on Large-Scale Graphs
Rui Xue
Graph Neural Networks (GNNs) have shown exceptional success in graph representation learning and a wide range of real-world applications. However, scaling deeper GNNs poses challen…
cs.LG2025
HGNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning
Rui Xue, Tianfu Wu
Graph Neural Networks (GNNs) struggle to balance heterophily and homophily in representation learning, a challenge further amplified in self-supervised settings. We propose HGN…