mlsanityData to 7 Oct 2026

Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network

arXiv 2105.02565 ↗First posted 6 May 2021cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 32 of its 83 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

32 of 83 references.

References · 32

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Improved Training of Wasserstein GANscited by 2,223
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  5. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  6. 2020Overfitting in adversarially robust deep learningcited by 504
  7. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  8. 2018Deep Learning on Graphs: A Surveycited by 393
  9. 2018MolGAN: An implicit generative model for small molecular graphscited by 392
  10. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 363
  11. 2018Graph R-CNN for Scene Graph Generationcited by 296
  12. 2019Efficient Graph Generation with Graph Recurrent Attention Networkscited by 231
20 more
  1. 2016Generative Multi-Adversarial Networkscited by 60
  2. 2019RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributescited by 56
  3. 2019DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesiscited by 36
  4. 2019Multi-marginal Wasserstein GANcited by 32
  5. 2020A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysiscited by 27
  6. 2019A Two-Step Graph Convolutional Decoder for Molecule Generationcited by 25
  7. 2020Pooling Regularized Graph Neural Network for fMRI Biomarker Analysiscited by 25
  8. 2019Multi-objective training of Generative Adversarial Networks with multiple discriminatorscited by 24
  9. 2020Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignmentcited by 24
  10. 2019Image-Conditioned Graph Generation for Road Network Extractioncited by 17
  11. 2020Graph Deconvolutional Generationcited by 16
  12. 2020Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templatescited by 16
  13. 2021A Graph VAE and Graph Transformer Approach to Generating Molecular Graphscited by 16
  14. 2017Stabilizing GAN Training with Multiple Random Projectionscited by 15
  15. 2020Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepointcited by 11
  16. 2019Graph Generation with Variational Recurrent Neural Networkcited by 7
  17. 2020Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observationcited by 6
  18. 2020Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graphcited by 5
  19. 2020Supervised Multi-topology Network Cross-diffusion for Population-driven Brain Network Atlas Estimationcited by 4
  20. 2017SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learningcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 3.

7 papers.

  1. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  2. 2025Recent Advances in Hypergraph Neural Networkscited by 7
  3. 2023TetCNN: Convolutional Neural Networks on Tetrahedral Meshescited by 5
  4. 2022Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topologycited by 1
  5. 2022Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classificationcited by 1
  6. 2025GraphTreeGen: Subtree-Centric Approach to Efficient and Supervised Graph Generationcited by 0
  7. 2025Multimodal Graph Neural Networks for Prognostic Modeling of Brain Network Reorganizationcited by 0