mlsanityData to 14 Oct 2026

Graph Neural Networks for Brain Graph Learning: A Survey

arXiv 2406.02594 ↗First posted 1 Jun 2024cs.LG cs.AIcited by 13

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Abstract (arXiv)

Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at urlhttps://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 30 of its 53 references that are in the map. First posted on arXiv 1 Jun 2024.

Builds on

30 of 53 references.

References · 30

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  3. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 516
  4. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
  5. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  6. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  7. 2020Molecular graph generation with Graph Neural Networkscited by 80
  8. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  9. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 75
  10. 2021A Survey on Graph Structure Learning: Progress and Opportunitiescited by 71
  11. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 67
  12. 2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 59
18 more
  1. 2020Spatio-Temporal Graph Convolution for Resting-State fMRI Analysiscited by 57
  2. 2019Graph Neural Network for Interpreting Task-fMRI Biomarkerscited by 40
  3. 2022Multi-modal Graph Learning for Disease Predictioncited by 34
  4. 2023Community-Aware Transformer for Autism Prediction in fMRI Connectomecited by 28
  5. 2022Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selectioncited by 26
  6. 2021Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosiscited by 20
  7. 2022Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learningcited by 15
  8. 2018Multi-View Multi-Graph Embedding for Brain Network Clustering Analysiscited by 14
  9. 2022Deep reinforcement learning guided graph neural networks for brain network analysiscited by 12
  10. 2022Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosiscited by 11
  11. 2023Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networkscited by 11
  12. 2023PTGB: Pre-Train Graph Neural Networks for Brain Network Analysiscited by 10
  13. 2019Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networkscited by 9
  14. 2017Multi-view Graph Embedding with Hub Detection for Brain Network Analysiscited by 8
  15. 2021Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Networkcited by 8
  16. 2023Transformer-Based Hierarchical Clustering for Brain Network Analysiscited by 8
  17. 2022Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Networkcited by 7
  18. 2023HDGL: A hierarchical dynamic graph representation learning model for brain disorder classificationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 6.

13 papers.

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  1. 2026Learning beyond Site Bias for OOD Generalization in Brain Networkscited by 2
  2. 2025BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysiscited by 1
  3. 2024Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learningcited by 0
  4. 2025SlepNet: Spectral Subgraph Representation Learning for Neural Dynamicscited by 0
  5. 2025Defining and Benchmarking a Data-Centric Design Space for Brain Graph Constructioncited by 0
  6. 2025Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networkscited by 0
  7. 2025Brain PathoGraph Learningcited by 0
  8. 2025Alzheimer's Disease Brain Network Miningcited by 0
  9. 2026Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classificationcited by 0
  10. 2026Taming Epilepsy: Mean Field Control of Whole-Brain Dynamicscited by 0
  11. 2026BrainDyn: A Sheaf Neural ODE for Forecasting and Generating Brain Dynamicscited by 0
  12. 2026MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classificationcited by 0
1 more
  1. 2026Interpretable Information-Decomposed Brain Graph Learning for fMRI-based Disease Diagnosiscited by 0