# Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network

arXiv: https://arxiv.org/abs/2105.02565 · HTML: https://mlsanity.com/paper/2105.02565/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 83 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 30 of 32)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Overfitting in adversarially robust deep learning](https://mlsanity.com/paper/2002.11569/) (2020)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation](https://mlsanity.com/paper/1806.02473/) (2018)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Efficient Graph Generation with Graph Recurrent Attention Networks](https://mlsanity.com/paper/1910.00760/) (2019)
- [Generative Multi-Adversarial Networks](https://mlsanity.com/paper/1611.01673/) (2016)
- [RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes](https://mlsanity.com/paper/1908.07269/) (2019)
- [DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis](https://mlsanity.com/paper/1904.12894/) (2019)
- [Multi-marginal Wasserstein GAN](https://mlsanity.com/paper/1911.00888/) (2019)
- [A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis](https://mlsanity.com/paper/2005.04573/) (2020)
- [A Two-Step Graph Convolutional Decoder for Molecule Generation](https://mlsanity.com/paper/1906.03412/) (2019)
- [Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis](https://mlsanity.com/paper/2007.14589/) (2020)
- [Multi-objective training of Generative Adversarial Networks with multiple discriminators](https://mlsanity.com/paper/1901.08680/) (2019)
- [Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment](https://mlsanity.com/paper/2006.12770/) (2020)
- [Image-Conditioned Graph Generation for Road Network Extraction](https://mlsanity.com/paper/1910.14388/) (2019)
- [Graph Deconvolutional Generation](https://mlsanity.com/paper/2002.07087/) (2020)
- [Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates](https://mlsanity.com/paper/2012.14131/) (2020)
- [A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs](https://mlsanity.com/paper/2104.04345/) (2021)
- [Stabilizing GAN Training with Multiple Random Projections](https://mlsanity.com/paper/1705.07831/) (2017)
- [Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint](https://mlsanity.com/paper/2009.13217/) (2020)
- [Graph Generation with Variational Recurrent Neural Network](https://mlsanity.com/paper/1910.01743/) (2019)
- [Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation](https://mlsanity.com/paper/2009.11110/) (2020)
- [Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph](https://mlsanity.com/paper/2009.11058/) (2020)

## Built on by (most cited first, 8 of 8)

- [Graph Neural Networks for Brain Graph Learning: A Survey](https://mlsanity.com/paper/2406.02594/) (2024)
- [Recent Advances in Hypergraph Neural Networks](https://mlsanity.com/paper/2503.07959/) (2025)
- [TetCNN: Convolutional Neural Networks on Tetrahedral Meshes](https://mlsanity.com/paper/2302.03830/) (2023)
- [Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology](https://mlsanity.com/paper/2207.09657/) (2022)
- [Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification](https://mlsanity.com/paper/2212.07790/) (2022)
- [GraphTreeGen: Subtree-Centric Approach to Efficient and Supervised Graph Generation](https://mlsanity.com/paper/2508.09710/) (2025)
- [Multimodal Graph Neural Networks for Prognostic Modeling of Brain Network Reorganization](https://mlsanity.com/paper/2512.06303/) (2025)
- [Structural-Functional Brain Connectivity Generation via Multimodal Hypergraph-based Flow Matching](https://mlsanity.com/paper/2610.02722/) (2026)

## 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.