Resting-state functional magnetic resonance imaging (rs-fMRI) enables the characterization of functional interactions among distributed brain regions and has shown promise for brain disorder diagnosis. However, existing deep learning methods predominantly rely on node-centric representations, where brain regions serve...
Deng-Yi Zhao, Zhi-Heng Zhou, Meng-Yao Zhou et al.· 0 citations
Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on labeled data, making them vulnerable to label noise. Despite advances in learning with label noise (LLN) and graph learning with label noise (G...
Meng-Yao Zhou, Zhi-Heng Zhou, Xiao Han et al.· 0 citations
This work investigates hypergraph oversmoothing from a dynamical-systems perspective and develops a reaction--diffusion framework for depth-resistant hypergraph learning, which introduces a reaction mechanism acting on the transverse component to compensate diffusion-induced dissipation and stabilize discriminative var...
A new descriptive homophily measure for general social hypergraphs where group interactions involve arbitrary number of individuals is proposed, and constraints of monotonic and majority homophily for two-class labels are established, providing a framework for analyzing homophily patterns.
Yunping Wang, Zhiheng Zhou, Mingwei Li et al.· Chaos· 0 citations
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