mlsanityData to 7 Oct 2026

NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics

arXiv 2306.06202 ↗First posted 9 Jun 2023cs.LG cs.AI q-bio.NCcited by 33

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Abstract (arXiv)

Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psychiatric disorders, and cognitive patterns. In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations. The potency of graph machine learning methods has been established across myriad domains, marking a transformative step in data interpretation and predictive modeling. Yet, despite their promise, the transposition of these techniques to the neuroimaging domain has been challenging due to the expansive number of potential preprocessing pipelines and the large parameter search space for graph-based dataset construction. In this paper, we introduce NeuroGraph, a collection of graph-based neuroimaging datasets, and demonstrated its utility for predicting multiple categories of behavioral and cognitive traits. We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking. Additionally, we provide generic frameworks for learning on both static and dynamic graphs. Our extensive experiments lead to several key observations. Notably, using correlation vectors as node features, incorporating larger number of regions of interest, and employing sparser graphs lead to improved performance. To foster further advancements in graph-based data driven neuroimaging analysis, we offer a comprehensive open-source Python package that includes the benchmark datasets, baseline implementations, model training, and standard evaluation.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 27 of its 67 references that are in the map. First posted on arXiv 9 Jun 2023.

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27 of 67 references.

References · 27

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  5. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  6. 2019Simplifying Graph Convolutional Networkscited by 1,470
  7. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  8. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  9. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  10. 2017Residual Gated Graph ConvNetscited by 341
  11. 2019Representation Learning for Dynamic Graphs: A Surveycited by 260
  12. 2020Design Space for Graph Neural Networkscited by 182
15 more
  1. 2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 131
  2. 2022Brain Network Transformercited by 92
  3. 2021Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attentioncited by 86
  4. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 73
  5. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  6. 2020Spatio-Temporal Graph Convolution for Resting-State fMRI Analysiscited by 57
  7. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
  8. 2020Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysiscited by 41
  9. 2019Graph Neural Network for Interpreting Task-fMRI Biomarkerscited by 40
  10. 2018Dynamic Graph Representation Learning via Self-Attention Networkscited by 39
  11. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  12. 2019Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Datacited by 30
  13. 2019ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI datacited by 27
  14. 2021Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivitycited by 9
  15. 2022Contrastive Graph Learning for Population-based fMRI Classificationcited by 3

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Citing papers per year: 2023: 5; 2024: 9; 2025: 15; 2026: 4.

33 papers.

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  1. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  2. 2023A Survey of Graph Unlearningcited by 12
  3. 2023Learning High-Order Relationships of Brain Regionscited by 10
  4. 2025Biologically Plausible Brain Graph Transformercited by 9
  5. 2025Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Helpcited by 8
  6. 2024BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signalscited by 6
  7. 2024NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomescited by 6
  8. 2025BACE: Behavior-Adaptive Connectivity Estimation for Interpretable Graphs of Neural Dynamicscited by 6
  9. 2024Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theorycited by 4
  10. 2025DCA: Graph-Guided Deep Embedding Clustering for Brain Atlasescited by 4
  11. 2025Large Connectome Model: An fMRI Foundation Model of Brain Connectomes Empowered by Brain-Environment Interaction in Multitask Learning Landscapecited by 4
  12. 2023Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentationcited by 3
21 more
  1. 2023A Generative Self-Supervised Framework using Functional Connectivity in fMRI Datacited by 2
  2. 2024Control-based Graph Embeddings with Data Augmentation for Contrastive Learningcited by 2
  3. 2024A Benchmark for Fairness-Aware Graph Learningcited by 2
  4. 2024Scalable Graph Compressed Convolutionscited by 2
  5. 2024Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretationscited by 2
  6. 2024BrainMAP: Learning Multiple Activation Pathways in Brain Networkscited by 2
  7. 2026Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaborationcited by 2
  8. 2024Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic reviewcited by 1
  9. 2025Graph Contrastive Learning for Connectome Classificationcited by 1
  10. 2025Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesiscited by 1
  11. 2025Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networkscited by 1
  12. 2025Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraintscited by 1
  13. 2026GeoDynamics: A Geometric State-Space Neural Network for Understanding Brain Dynamics on Riemannian Manifoldscited by 1
  14. 2025On the Limits of Applying Graph Transformers for Brain Connectome Classificationcited by 0
  15. 2025HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystalscited by 0
  16. 2025GraphTreeGen: Subtree-Centric Approach to Efficient and Supervised Graph Generationcited by 0
  17. 2025Defining and Benchmarking a Data-Centric Design Space for Brain Graph Constructioncited by 0
  18. 2025Brain PathoGraph Learningcited by 0
  19. 2025Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomescited by 0
  20. 2026Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classificationcited by 0
  21. 2026Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlasescited by 0