3 papers
cs.LG2026
ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning
Rui Xue, Tianfu Wu
Multimodal graph learning requires jointly training over graph structure and heterogeneous node attributes, yet existing methods largely decouple these processes: prior multimodal…
cs.LG2025
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
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…