multi-scale-hypergraph-brain-connectivity

name	multi-scale-hypergraph-brain-connectivity
description	Multi-scale hypergraph learning (MuHL) methodology for high-order brain connectivity analysis beyond pairwise GNNs. Accepted to ICML 2026. Use for: brain network analysis, neurodegenerative disease classification (Alzheimer's, Parkinson's), higher-order functional connectivity, hypergraph neural networks.
license	Complete terms in LICENSE.txt
metadata	{"arxiv_id":"2606.03310","published":"2026-06-03","authors":"Jaeyoon Sim, Soojin Hwang, Seunghun Baek, Guorong Wu, Won Hwa Kim","conference":"ICML 2026","tags":["brain-network","hypergraph","multi-scale","neurodegenerative","Alzheimer's","Parkinson's"]}

MuHL: Multi-Scale Hypergraph Learning for High-Order Brain Connectivity

Paper: "Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis" (arXiv:2606.03310, ICML 2026) Authors: Jaeyoon Sim, Soojin Hwang, Seunghun Baek, Guorong Wu, Won Hwa Kim

Core Problem

Graph-based models for brain network analysis primarily focus on pairwise interactions (edge = connection between 2 nodes). This misses higher-order dependencies across 3+ brain regions that are critical for understanding neurodegenerative disease progression (Alzheimer's, Parkinson's).

MuHL Methodology

Architecture

1. Hierarchical Node Feature Construction: Build multi-resolution graph signals at different scales of brain network granularity
2. Adaptive Multi-Scale Hyperedge Learning: Dynamically construct hyperedges over multi-resolution graph signals (not predefined)
3. Continuous Hyperedge Construction: Learn hyperedges continuously rather than discrete predefined sets

Key Innovation: Dynamic vs Predefined Hyperedges

Approach	Hyperedge Source	Flexibility	Multi-Resolution
Traditional hypergraphs	Predefined (fixed)	Low	No
Weight-only learning	Weights of fixed hyperedges	Medium	No
MuHL (this work)	Learned dynamically	High	Yes

Technical Details

• Multi-resolution graph signals: Decompose brain network features at multiple scales
• Continuous hyperedge construction: Soft assignment of nodes to hyperedges via learnable parameters
• Hierarchical aggregation: Pool features across scales to capture both local and global patterns

Application Results

• Alzheimer's Disease classification: Improved performance across different disease stages
• Parkinson's Disease classification: Consistent improvement over graph-based baselines
• ROI identification: Learned hyperedges identify key regions and group-wise interactions associated with disease progression

Reusable Patterns

Pattern 1: Higher-Order Connectivity Modeling

When pairwise GNNs underperform on brain network tasks:

1. Construct multi-scale graph representations
2. Learn hyperedges adaptively (not predefined)
3. Aggregate across hyperedge scales hierarchically
4. Identify disease-relevant ROI groups from learned hyperedges

Pattern 2: Multi-Resolution Brain Network Analysis

Input: Brain ROI features + connectivity matrix
├── Scale 1: Fine-grained (individual ROIs)
├── Scale 2: Medium-grained (ROI clusters)
└── Scale 3: Coarse-grained (network modules)
    → Learn hyperedges spanning all scales
    → Hierarchical message passing
    → Disease classification + ROI importance

Comparison with Existing Methods

Method	Interaction Order	Hyperedge Learning	Disease Classification
GCN/GAT	Pairwise (2-node)	N/A	Baseline
Predefined hypergraph	Fixed higher-order	Weights only	Medium
MuHL	Adaptive higher-order	Full structure learning	Best

Pitfalls

• Predefined hyperedges underutilized: Manually defining hyperedges from known anatomical regions misses dynamic disease-specific patterns. Always learn hyperedges adaptively from data.
• Scale mismatch: Using single-scale features ignores multi-resolution disease signatures. Construct hierarchical features at fine/medium/coarse granularity.
• Hyperedge cardinality imbalance: Overly large hyperedges (10+ nodes) dilute signal; overly small (2-3 nodes) revert to pairwise. Balance via learnable cardinality constraints.

Activation

hypergraph brain connectivity, higher-order brain network, multi-scale hypergraph, neurodegenerative disease classification, Alzheimer's GNN, Parkinson's brain network, MuHL, ICML 2026, brain ROI analysis, multi-resolution brain network

Related Skills

• [[brain-graph-neural]] - Graph neural networks for brain connectivity
• [[brain-higher-order-structures]] - Higher-order brain network analysis with simplicial complexes
• [[higher-order-brain-networks]] - Higher-order brain network analysis using topological signatures
• [[dcho-higher-order-brain-connectivity]] - DCHO higher-order brain connectivity prediction