# BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis

arXiv: https://arxiv.org/abs/2511.17604 · HTML: https://mlsanity.com/paper/2511.17604/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis](https://mlsanity.com/paper/2207.00813/) (2022)
- [Graph Neural Network for Interpreting Task-fMRI Biomarkers](https://mlsanity.com/paper/1907.01661/) (2019)
- [Community-Aware Transformer for Autism Prediction in fMRI Connectome](https://mlsanity.com/paper/2307.10181/) (2023)
- [BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck](https://mlsanity.com/paper/2205.03612/) (2022)
- [Small-World Brain Networks Revisited](https://mlsanity.com/paper/1608.05665/) (2016)
- [Multi-scale brain networks](https://mlsanity.com/paper/1608.08828/) (2016)
- [Graph Neural Networks for Brain Graph Learning: A Survey](https://mlsanity.com/paper/2406.02594/) (2024)
- [Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification](https://mlsanity.com/paper/2409.10944/) (2024)

## Built on by (most cited first, 1 of 1)

- [Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis](https://mlsanity.com/paper/2606.29695/) (2026)

## Abstract (arXiv)

Graph Transformer shows remarkable potential in brain network analysis due to its ability to model graph structures and complex node relationships. Most existing methods typically model the brain as a flat network, ignoring its modular structure, and their attention mechanisms treat all brain region connections equally, ignoring distance-related node connection patterns. However, brain information processing is a hierarchical process that involves local and long-range interactions between brain regions, interactions between regions and sub-functional modules, and interactions among functional modules themselves. This hierarchical interaction mechanism enables the brain to efficiently integrate local computations and global information flow, supporting the execution of complex cognitive functions. To address this issue, we propose BrainHGT, a hierarchical Graph Transformer that simulates the brain's natural information processing from local regions to global communities. Specifically, we design a novel long-short range attention encoder that utilizes parallel pathways to handle dense local interactions and sparse long-range connections, thereby effectively alleviating the over-globalizing issue. To further capture the brain's modular architecture, we designe a prior-guided clustering module that utilizes a cross-attention mechanism to group brain regions into functional communities and leverage neuroanatomical prior to guide the clustering process, thereby improving the biological plausibility and interpretability. Experimental results indicate that our proposed method significantly improves performance of disease identification, and can reliably capture the sub-functional modules of the brain, demonstrating its interpretability.