- This is the official PyTorch implementation of Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis.
Understanding complex interactions between brain regions is critical for early neurodegenerative disease classification such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). While graph-based models are widely used to analyze brain networks, most existing approaches primarily focus on pairwise interactions between directly connected nodes, limiting their ability to capture higher-order dependencies across multiple regions. Although hypergraph-based methods have been proposed to model higher-order relations, many rely on predefined hyperedges or restrict learning to hyperedge weights, reducing flexibility and limiting their capacity to capture multi-resolution structural patterns. In this regard, we introduce an adaptive multi-scale hyperedge learning framework, i.e., MuHL, which constructs hierarchical node features and dynamically learns high-order interaction through continuous hyperedge construction over multi-resolution graph signals. Extensive experiments on multiple brain network benchmarks demonstrate that MuHL consistently improves disease classification performance across different stages, and further identifies key regions of interest (ROIs) and their groupwise interactions from the learned hyperedges that are associated with disease progression, highlighting its potential as a powerful tool for brain network analysis with neurodegenerative disorders.
If you find our work useful for your research, please cite the our paper:
@inproceedings{sim2026learning,
title={Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis},
author={Sim, Jaeyoon and Hwang, Soojin, and Baek, Seunghun and Wu, Guorong and Kim, Won Hwa},
booktitle={Forty-third International Conference on Machine Learning},
year={2026}
}