# InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

arXiv: https://arxiv.org/abs/1903.04233 · HTML: https://mlsanity.com/paper/1903.04233/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 30 of 39)

- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2203.05880/) (2022)
- [Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective](https://mlsanity.com/paper/2112.14531/) (2021)
- [GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis](https://mlsanity.com/paper/2203.09034/) (2022)
- [Node-oriented Spectral Filtering for Graph Neural Networks](https://mlsanity.com/paper/2212.03654/) (2022)
- [Heterogeneous Graph Learning for Multi-modal Medical Data Analysis](https://mlsanity.com/paper/2211.15158/) (2022)
- [RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data](https://mlsanity.com/paper/2103.00221/) (2021)
- [Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again](https://mlsanity.com/paper/2210.08122/) (2022)
- [S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences](https://mlsanity.com/paper/2304.07515/) (2023)
- [IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction](https://mlsanity.com/paper/2103.15587/) (2021)
- [SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks](https://mlsanity.com/paper/2112.11628/) (2021)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction](https://mlsanity.com/paper/2406.14455/) (2024)
- [MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning](https://mlsanity.com/paper/2312.14574/) (2023)
- [Heterogeneous Graph-Based Multimodal Brain Network Learning](https://mlsanity.com/paper/2110.08465/) (2021)
- [Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications](https://mlsanity.com/paper/2204.00323/) (2022)
- [GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference](https://mlsanity.com/paper/2104.03597/) (2021)
- [Multimodal brain age estimation using interpretable adaptive population-graph learning](https://mlsanity.com/paper/2307.04639/) (2023)
- [A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression](https://mlsanity.com/paper/2309.14816/) (2023)
- [Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis](https://mlsanity.com/paper/2502.20769/) (2025)
- [HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification](https://mlsanity.com/paper/2311.02903/) (2023)
- [Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups](https://mlsanity.com/paper/2401.05478/) (2024)
- [HyperGALE: ASD Classification via Hypergraph Gated Attention with Learnable Hyperedges](https://mlsanity.com/paper/2403.14484/) (2024)
- [Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks](https://mlsanity.com/paper/2406.17073/) (2024)
- [PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs](https://mlsanity.com/paper/2410.10089/) (2024)
- [NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders](https://mlsanity.com/paper/2502.18786/) (2025)
- [NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images](https://mlsanity.com/paper/2103.05385/) (2021)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2107.00206/) (2021)

## 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.