mlsanityData to 7 Oct 2026

InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

arXiv 1903.04233 ↗First posted 11 Mar 2019cs.LG stat.MLcited by 39

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Abstract (arXiv)

Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GCNs) in particular have been explored on a wide variety of problems such as disease prediction, segmentation, and matrix completion by leveraging large, multimodal datasets. In this paper, we introduce a new spectral domain architecture for deep learning on graphs for disease prediction. The novelty lies in defining geometric 'inception modules' which are capable of capturing intra- and inter-graph structural heterogeneity during convolutions. We design filters with different kernel sizes to build our architecture. We show our disease prediction results on two publicly available datasets. Further, we provide insights on the behaviour of regular GCNs and our proposed model under varying input scenarios on simulated data.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2019.

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Citing papers per year: 2021: 10; 2022: 9; 2023: 7; 2024: 7; 2025: 3; 2026: 3.

39 papers.

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  1. 2021Graph Neural Networks in Network Neurosciencecited by 91
  2. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  3. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  4. 2022Multi-modal Graph Learning for Disease Predictioncited by 34
  5. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  6. 2022GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysiscited by 19
  7. 2022Node-oriented Spectral Filtering for Graph Neural Networkscited by 17
  8. 2022Heterogeneous Graph Learning for Multi-modal Medical Data Analysiscited by 16
  9. 2021RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Datacited by 14
  10. 2022Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Againcited by 11
  11. 2023S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondencescited by 11
  12. 2021IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease predictioncited by 10
27 more
  1. 2021SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networkscited by 10
  2. 2022Unsupervised pre-training of graph transformers on patient population graphscited by 9
  3. 2024MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Predictioncited by 9
  4. 2023MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learningcited by 7
  5. 2021Heterogeneous Graph-Based Multimodal Brain Network Learningcited by 5
  6. 2022Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applicationscited by 4
  7. 2021GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inferencecited by 3
  8. 2023Multimodal brain age estimation using interpretable adaptive population-graph learningcited by 3
  9. 2023A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regressioncited by 2
  10. 2025Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosiscited by 2
  11. 2023HDGL: A hierarchical dynamic graph representation learning model for brain disorder classificationcited by 1
  12. 2024Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groupscited by 1
  13. 2024HyperGALE: ASD Classification via Hypergraph Gated Attention with Learnable Hyperedgescited by 1
  14. 2024Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networkscited by 1
  15. 2024PromptGCN: Bridging Subgraph Gaps in Lightweight GCNscited by 1
  16. 2025NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorderscited by 1
  17. 2021NaroNet: Discovery of tumor microenvironment elements from highly multiplexed imagescited by 0
  18. 2021Multi-modal Graph Learning for Disease Predictioncited by 0
  19. 2022Improving the Level of Autism Discrimination through GraphRNN Link Predictioncited by 0
  20. 2023Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discoverycited by 0
  21. 2023Classification of developmental and brain disorders via graph convolutional aggregationcited by 0
  22. 2024MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysiscited by 0
  23. 2024Multi-SIGATnet: A multimodal schizophrenia MRI classification algorithm using sparse interaction mechanisms and graph attention networkscited by 0
  24. 2025A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorderscited by 0
  25. 2026MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosiscited by 0
  26. 2026Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learningcited by 0
  27. 2026Target-Aware State-Adaptive p-Dirichlet Graph Neural Regression for Non-Invasive Body-Composition Estimationcited by 0