3 papers
cs.LG2026
Geometric Imbalance in Semi-Supervised Node Classification
Liang Yan, Shengzhong Zhang, Bisheng Li +6
Class imbalance in graph data presents a significant challenge for effective node classification, particularly in semi-supervised scenarios. In this work, we formally introduce the…
cs.LG2025
Implicit vs Unfolded Graph Neural Networks
Yongyi Yang, Tang Liu, Yangkun Wang +2
It has been observed that message-passing graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient/scalable modeling of long-range depende…
cs.LG2025
MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy
Haitian Jiang, Renjie Liu, Zengfeng Huang +5
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that…