mlsanityData to 14 Oct 2026

PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis

arXiv 2305.14376 ↗First posted 20 May 2023q-bio.NC cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis have shown a growing interest in the interactions between brain regions of interest (ROIs) and their impact on neural development and disorder diagnosis. As a powerful deep model for analyzing graph-structured data, Graph Neural Networks (GNNs) have been applied for brain network analysis. However, training deep models requires large amounts of labeled data, which is often scarce in brain network datasets due to the complexities of data acquisition and sharing restrictions. To make the most out of available training data, we propose PTGB, a GNN pre-training framework that captures intrinsic brain network structures, regardless of clinical outcomes, and is easily adaptable to various downstream tasks. PTGB comprises two key components: (1) an unsupervised pre-training technique designed specifically for brain networks, which enables learning from large-scale datasets without task-specific labels; (2) a data-driven parcellation atlas mapping pipeline that facilitates knowledge transfer across datasets with different ROI systems. Extensive evaluations using various GNN models have demonstrated the robust and superior performance of PTGB compared to baseline methods.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 62 of its 100 references that are in the map. First posted on arXiv 20 May 2023.

Builds on

62 of 100 references.

References · 62

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  6. 2017Graph Attention Networkscited by 8,923
  7. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  8. 2017Inductive Representation Learning on Large Graphscited by 6,320
  9. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  10. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  11. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  12. 2018How Powerful are Graph Neural Networks?cited by 4,393
38 more
  1. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  3. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  4. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
  5. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  7. 2016Variational Graph Auto-Encoderscited by 1,382
  8. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,273
  9. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  10. 2018Deep Graph Infomaxcited by 1,106
  11. 2019Strategies for Pre-training Graph Neural Networkscited by 971
  12. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  13. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  14. 2021FILIP: Fine-grained Interactive Language-Image Pre-Trainingcited by 627
  15. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  16. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 463
  17. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  18. 2020Parameterized Explainer for Graph Neural Networkcited by 426
  19. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 396
  20. 2019Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAMLcited by 359
  21. 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 303
  22. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  23. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  24. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
  25. 2020Data Augmentation for Graph Neural Networkscited by 203
  26. 2016Using Fast Weights to Attend to the Recent Pastcited by 170
  27. 2019Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Modelcited by 102
  28. 2022Brain Network Transformercited by 94
  29. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  30. 2020BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervisioncited by 91
  31. 2021Graph Barlow Twins: A self-supervised representation learning framework for graphscited by 85
  32. 2020Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approachcited by 83
  33. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 75
  34. 2021ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learningcited by 72
  35. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 67
  36. 2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 59
  37. 2020GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detectioncited by 54
  38. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49

50 most cited of 62 All 62 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

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

10 papers.

  1. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  2. 2023R-Mixup: Riemannian Mixup for Biological Networkscited by 9
  3. 2023Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Trainingcited by 8
  4. 2024RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregationcited by 4
  5. 2026NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoningcited by 3
  6. 2024Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learningcited by 2
  7. 2025A Survey of Cross-domain Graph Learning: Progress and Future Directionscited by 2
  8. 2025A Note on Graphon-Signal Analysis of Graph Neural Networkscited by 1
  9. 2026FCN-LLM: Empower LLM for Brain Functional Connectivity Network Understanding via Graph-level Multi-task Instruction Tuningcited by 0
  10. 2026Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlasescited by 0