mlsanityData to 14 Oct 2026

Spectral Graph Convolutions for Population-based Disease Prediction

arXiv 1703.03020 ↗First posted 8 Mar 2017stat.ML cs.LGcited by 42

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Abstract (arXiv)

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Mar 2017.

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Citing papers per year: 2021: 13; 2022: 11; 2023: 10; 2024: 3; 2025: 2; 2026: 3.

42 papers.

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  1. 2021Graph Neural Networks in Network Neurosciencecited by 92
  2. 2021Lorentzian Graph Convolutional Networkscited by 68
  3. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  4. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  5. 2022Multi-modal Graph Learning for Disease Predictioncited by 34
  6. 2022BolT: Fused Window Transformers for fMRI Time Series Analysiscited by 23
  7. 2022Node-oriented Spectral Filtering for Graph Neural Networkscited by 17
  8. 2022Heterogeneous Graph Learning for Multi-modal Medical Data Analysiscited by 16
  9. 2021Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identificationcited by 15
  10. 2021RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Datacited by 14
  11. 2022Data-Driven Network Neuroscience: On Data Collection and Benchmarkcited by 12
  12. 2023S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondencescited by 11
30 more
  1. 2021IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease predictioncited by 10
  2. 2021U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Predictioncited by 4
  3. 2022Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applicationscited by 4
  4. 2023Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric researchcited by 4
  5. 2021GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inferencecited by 3
  6. 2021Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learningcited by 3
  7. 2022Contrastive Graph Learning for Population-based fMRI Classificationcited by 3
  8. 2023BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Modelcited by 2
  9. 2025Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosiscited by 2
  10. 2026BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosiscited by 2
  11. 2021Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneitycited by 1
  12. 2021Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networkscited by 1
  13. 2022Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volumecited by 1
  14. 2023Extended Graph Assessment Metrics for Graph Neural Networkscited by 1
  15. 2023Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disordercited by 1
  16. 2023HDGL: A hierarchical dynamic graph representation learning model for brain disorder classificationcited by 1
  17. 2023AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicinecited by 1
  18. 2024Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groupscited by 1
  19. 2021Modelling Urban Dynamics with Multi-Modal Graph Convolutional Networkscited by 0
  20. 2021Multi-modal Graph Learning for Disease Predictioncited by 0
  21. 2022An overview of deep learning in medical imagingcited by 0
  22. 2022Improving the Level of Autism Discrimination through GraphRNN Link Predictioncited by 0
  23. 2023Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discoverycited by 0
  24. 2023Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structurecited by 0
  25. 2023Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosiscited by 0
  26. 2024MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysiscited by 0
  27. 2024Network Representation Learning for Biophysical Neural Network Analysiscited by 0
  28. 2025Symbolic Graph Intelligence: Hypervector Message Passing for Learning Graph-Level Patterns with Tsetlin Machinescited by 0
  29. 2026Topology Matters: A Cautionary Case Study of Graph SSL on Neuro-Inspired Benchmarkscited by 0
  30. 2026MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosiscited by 0