1 citations · 1 across the 8 of their papers we have counts for
9 papers
Edge-centric Brain Transformer: An Edge-centric Functional Connectivity Learning Framework for fMRI-based Brain Disorder Diagnosis
Dengyi Zhao, Zhiheng Zhou, Mengyao Zhou +2
Resting-state functional magnetic resonance imaging (rs-fMRI) enables the characterization of functional interactions among distributed brain regions and has shown promise for brai…
Towards Trustworthy Hypergraph Neural Networks under Label Noise
Mengyao Zhou, Zhiheng Zhou, Xiao Han +1
Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on lab…
Interpretable Information-Decomposed Brain Graph Learning for fMRI-based Disease Diagnosis
Dengyi Zhao, Zhiheng Zhou, Zihan Wang +2
Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided diagnosis, yet most existing appr…
From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks
Zhiheng Zhou, Mengyao Zhou, Yancheng Chen +3
Higher-order couplings enhance the expressive power of hypergraph neural networks (HGNNs), but they also intensify representation collapse in deep propagation due to strong multi-w…
Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation
Zhiheng Zhou, Mengyao Zhou, Dengyi Zhao +2
Hypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored. Unlike pairwise graphs, uncert…
Hypergraph Neural Diffusion: A PDE-Inspired Framework for Hypergraph Message Passing
Zhiheng Zhou, Mengyao Zhou, Xixun Lin +2
Hypergraph neural networks (HGNNs) have shown remarkable potential in modeling high-order relationships that naturally arise in many real-world data domains. However, existing HGNN…
Tackling Over-smoothing on Hypergraphs: A Ricci Flow-guided Neural Diffusion Approach
Mengyao Zhou, Zhiheng Zhou, Xiao Han +3
Hypergraph neural networks (HGNNs) have demonstrated strong capabilities in modeling complex higher-order relationships. However, existing HGNNs often suffer from over-smoothing as…
Extracting Interpretable Higher-Order Topological Features across Multiple Scales for Alzheimer's Disease Classification
Dengyi Zhao, Shanyong Li, Yunping Wang +4
Brain network topology, derived from functional magnetic resonance imaging (fMRI), holds promise for improving Alzheimer's disease (AD) diagnosis. Current methods primarily focus o…
HOI-Brain: a novel multi-channel transformers framework for brain disorder diagnosis by accurately extracting signed higher-order interactions from fMRI
Dengyi Zhao, Zhiheng Zhou, Guiying Yan +2
Accurately characterizing higher-order interactions of brain regions and extracting interpretable organizational patterns from Functional Magnetic Resonance Imaging data is crucial…