| 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
- Hierarchical Node Feature Construction: Build multi-resolution graph signals at different scales of brain network granularity
- Adaptive Multi-Scale Hyperedge Learning: Dynamically construct hyperedges over multi-resolution graph signals (not predefined)
- 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
