3 papers
cs.LG2025
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Xingbo Fu, Zihan Chen, Yinhan He +4
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data…
cs.AI2025
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
Song Wang, Zhenyu Lei, Zhen Tan +8
Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and hum…
cs.LG2024
ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data
Zhenyu Lei, Yushun Dong, Jundong Li +1
Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningf…