# RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

arXiv: https://arxiv.org/abs/2103.00221 · HTML: https://mlsanity.com/paper/2103.00221/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 89 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 30 of 38)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [SIGN: Scalable Inception Graph Neural Networks](https://mlsanity.com/paper/2004.11198/) (2020)
- [Revisiting Graph Neural Networks: All We Have is Low-Pass Filters](https://mlsanity.com/paper/1905.09550/) (2019)
- [Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images](https://mlsanity.com/paper/1804.04488/) (2018)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Directed Graph Convolutional Network](https://mlsanity.com/paper/2004.13970/) (2020)
- [Imbalanced Deep Learning by Minority Class Incremental Rectification](https://mlsanity.com/paper/1804.10851/) (2018)
- [L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks](https://mlsanity.com/paper/2003.13606/) (2020)
- [Label Efficient Semi-Supervised Learning via Graph Filtering](https://mlsanity.com/paper/1901.09993/) (2019)
- [Spectral Graph Convolutions for Population-based Disease Prediction](https://mlsanity.com/paper/1703.03020/) (2017)
- [InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/1903.04233/) (2019)
- [Latent-Graph Learning for Disease Prediction](https://mlsanity.com/paper/2003.13620/) (2020)
- [SumGraph: Video Summarization via Recursive Graph Modeling](https://mlsanity.com/paper/2007.08809/) (2020)
- [Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks](https://mlsanity.com/paper/2003.11702/) (2020)
- [Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction](https://mlsanity.com/paper/2009.02759/) (2020)
- [Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification](https://mlsanity.com/paper/1704.07487/) (2017)
- [Class Imbalance Problem in Data Mining Review](https://mlsanity.com/paper/1305.1707/) (2013)
- [A Cost-Sensitive Deep Belief Network for Imbalanced Classification](https://mlsanity.com/paper/1804.10801/) (2018)
- [PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN](https://mlsanity.com/paper/1911.12046/) (2019)

## Built on by (most cited first, 14 of 14)

- [Topology-Imbalance Learning for Semi-Supervised Node Classification](https://mlsanity.com/paper/2110.04099/) (2021)
- [Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks](https://mlsanity.com/paper/2111.08202/) (2021)
- [GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis](https://mlsanity.com/paper/2203.09034/) (2022)
- [LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection](https://mlsanity.com/paper/2503.01900/) (2025)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks](https://mlsanity.com/paper/2302.14061/) (2023)
- [Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation](https://mlsanity.com/paper/2210.09549/) (2022)
- [Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection](https://mlsanity.com/paper/2302.06900/) (2023)
- [Enhancing medical vision-language contrastive learning via inter-matching relation modelling](https://mlsanity.com/paper/2401.10501/) (2024)
- [GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference](https://mlsanity.com/paper/2104.03597/) (2021)
- [Imbalanced Node Processing Method in Graph Neural Network Classification Task](https://mlsanity.com/paper/2209.08514/) (2022)
- [Class Unbiasing for Generalization in Medical Diagnosis](https://mlsanity.com/paper/2508.06943/) (2025)
- [GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance](https://mlsanity.com/paper/2511.10022/) (2025)
- [GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance](https://mlsanity.com/paper/2601.19352/) (2026)

## Abstract (arXiv)

Disease prediction is a well-known classification problem in medical applications. GCNs provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis of the rare positive cases among all the patients is vital in a healthcare system. In conventional methods, such imbalance is tackled by assigning appropriate weights to classes in the loss function which is still dependent on the relative values of weights, sensitive to outliers, and in some cases biased towards the minor class(es). In this paper, we propose a Re-weighted Adversarial Graph Convolutional Network (RA-GCN) to prevent the graph-based classifier from emphasizing the samples of any particular class. This is accomplished by associating a graph-based neural network to each class, which is responsible for weighting the class samples and changing the importance of each sample for the classifier. Therefore, the classifier adjusts itself and determines the boundary between classes with more attention to the important samples. The parameters of the classifier and weighting networks are trained by an adversarial approach. We show experiments on synthetic and three publicly available medical datasets. RA-GCN demonstrates the superiority compared to recent methods in identifying the patient's status on all three datasets. The detailed analysis is provided as quantitative and qualitative experiments on synthetic datasets.