# Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint

arXiv: https://arxiv.org/abs/2009.13217 · HTML: https://mlsanity.com/paper/2009.13217/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations](https://mlsanity.com/paper/2104.03895/) (2021)
- [Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network](https://mlsanity.com/paper/2105.02565/) (2021)
- [Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping](https://mlsanity.com/paper/2107.06281/) (2021)
- [Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory](https://mlsanity.com/paper/2110.11237/) (2021)
- [One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction](https://mlsanity.com/paper/2110.11238/) (2021)
- [A Comparative Study of Machine Learning Methods for Predicting the Evolution of Brain Connectivity from a Baseline Timepoint](https://mlsanity.com/paper/2109.07739/) (2021)
- [A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint](https://mlsanity.com/paper/2110.03535/) (2021)
- [Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data](https://mlsanity.com/paper/2401.01383/) (2024)
- [CogGNN: Cognitive Graph Neural Networks in Generative Connectomics](https://mlsanity.com/paper/2509.10864/) (2025)
- [Reservoir-Based Graph Convolutional Networks](https://mlsanity.com/paper/2603.24131/) (2026)

## Abstract (arXiv)

Learning how to predict the brain connectome (i.e. graph) development and aging is of paramount importance for charting the future of within-disorder and cross-disorder landscape of brain dysconnectivity evolution. Indeed, predicting the longitudinal (i.e., time-dependent ) brain dysconnectivity as it emerges and evolves over time from a single timepoint can help design personalized treatments for disordered patients in a very early stage. Despite its significance, evolution models of the brain graph are largely overlooked in the literature. Here, we propose EvoGraphNet, the first end-to-end geometric deep learning-powered graph-generative adversarial network (gGAN) for predicting time-dependent brain graph evolution from a single timepoint. Our EvoGraphNet architecture cascades a set of time-dependent gGANs, where each gGAN communicates its predicted brain graphs at a particular timepoint to train the next gGAN in the cascade at follow-up timepoint. Therefore, we obtain each next predicted timepoint by setting the output of each generator as the input of its successor which enables us to predict a given number of timepoints using only one single timepoint in an end- to-end fashion. At each timepoint, to better align the distribution of the predicted brain graphs with that of the ground-truth graphs, we further integrate an auxiliary Kullback-Leibler divergence loss function. To capture time-dependency between two consecutive observations, we impose an l1 loss to minimize the sparse distance between two serialized brain graphs. A series of benchmarks against variants and ablated versions of our EvoGraphNet showed that we can achieve the lowest brain graph evolution prediction error using a single baseline timepoint. Our EvoGraphNet code is available at http://github.com/basiralab/EvoGraphNet.