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Many biomedical data exhibit intrinsic graph-like properties, making graph neural networks (GNNs) widely adopted modeling tools. The brain arterial network (BAN) represents the most complex arterial network in humans, where conventional GNNs struggle to capture critical long-range relationships. Recent graph transformers have enabled modeling of these long-range dependencies through attention mechanisms; however, they face challenges in incorporating hierarchical information, especially when strong anatomical priors exist within the graph structure. While some approaches have attempted to integrate hierarchical information into graph transformers, they primarily focus on node feature aggregation, despite BAN’s most clinically significant features residing in edges rather than nodes. To address these limitations, we propose a hierarchical graph transformer (HGT) with edge-aware structural encoding that better incorporates anatomical and multi-scale structural information. Our approach achieves state-of-the-art performance across all 11 tasks. This work lays the foundation for individualized risk assessment that complements traditional systemic risk evaluation methods.

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Edge-Aware Hierarchical Graph Transformer to Decode Brain Arterial Network

  • 会议论文

  • 发表日期：

    2026年

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  • Kaiyu Zhang,
  • Li Chen,
  • Wenjin Liu,
  • Taewon Kim,
  • Xin Wang,
  • Yin Guo,
  • Zhiwei Tan,
  • Zhensen Chen,
  • Angie Tang,
  • Xihai Zhao,
  • Thomas S. Hatsukami,
  • Mahmud Mossa-Basha,
  • Niranjan Balu,
  • Chun Yuan

摘要

Many biomedical data exhibit intrinsic graph-like properties, making graph neural networks (GNNs) widely adopted modeling tools. The brain arterial network (BAN) represents the most complex arterial network in humans, where conventional GNNs struggle to capture critical long-range relationships. Recent graph transformers have enabled modeling of these long-range dependencies through attention mechanisms; however, they face challenges in incorporating hierarchical information, especially when strong anatomical priors exist within the graph structure. While some approaches have attempted to integrate hierarchical information into graph transformers, they primarily focus on node feature aggregation, despite BAN’s most clinically significant features residing in edges rather than nodes. To address these limitations, we propose a hierarchical graph transformer (HGT) with edge-aware structural encoding that better incorporates anatomical and multi-scale structural information. Our approach achieves state-of-the-art performance across all 11 tasks. This work lays the foundation for individualized risk assessment that complements traditional systemic risk evaluation methods.