mlsanityData to 7 Oct 2026

Transformer-Based Hierarchical Clustering for Brain Network Analysis

arXiv 2305.04142 ↗First posted 6 May 2023cs.LG cs.CV cs.NEcited by 8

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Abstract (arXiv)

Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain system, differences in neuronal connection strengths parcellate the brain into various functional modules (network communities), which are critical for brain analysis. However, identifying such communities within the brain has been a nontrivial issue due to the complexity of neuronal interactions. In this work, we propose a novel interpretable transformer-based model for joint hierarchical cluster identification and brain network classification. Extensive experimental results on real-world brain network datasets show that with the help of hierarchical clustering, the model achieves increased accuracy and reduced runtime complexity while providing plausible insight into the functional organization of brain regions. The implementation is available at https://github.com/DDVD233/THC.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 6 May 2023.

Builds on

18 of 41 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  6. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  7. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  8. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  9. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
  10. 2022Brain Network Transformercited by 92
  11. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 73
  12. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 66
6 more
  1. 2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 58
  2. 2022Higher-order Clustering and Pooling for Graph Neural Networkscited by 26
  3. 2021Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosiscited by 20
  4. 2022Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learningcited by 15
  5. 2018Multi-View Multi-Graph Embedding for Brain Network Clustering Analysiscited by 14
  6. 2022Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networkscited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  2. 2024Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identificationcited by 12
  3. 2025A Survey of Graph Transformers: Architectures, Theories and Applicationscited by 12
  4. 2023PTGB: Pre-Train Graph Neural Networks for Brain Network Analysiscited by 10
  5. 2023R-Mixup: Riemannian Mixup for Biological Networkscited by 8
  6. 2024Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusioncited by 6
  7. 2025Atlas-free Brain Network Transformercited by 0
  8. 2026MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classificationcited by 0