187 citations · 1.2k across the 72 of their papers we have counts for
16 papers · 1 filter
Graph Federated Learning with Hidden Representation Sharing
Shuang Wu, Mingxuan Zhang, Yuantong Li +2
Learning on Graphs (LoG) is widely used in multi-client systems when each client has insufficient local data, and multiple clients have to share their raw data to learn a model of…
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
Yue Yu, Xuan Kan, Hejie Cui +9
Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopt…
Brain Network Transformer
Xuan Kan, Wei Dai, Hejie Cui +3
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transform…
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
Qiuchen Zhang, Hong kyu Lee, Jing Ma +3
Graph Neural Networks (GNNs) have achieved great success in learning with graph-structured data. Privacy concerns have also been raised for the trained models which could expose th…
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Hejie Cui, Wei Dai, Yanqiao Zhu +3
Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional…
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks
Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi +2
Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowl…