# Graph Neural Networks in Network Neuroscience

arXiv: https://arxiv.org/abs/2106.03535 · HTML: https://mlsanity.com/paper/2106.03535/

## Key facts

- Cited by 92 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 127 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 30 of 44)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [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)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [A Fair Comparison of Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/1912.09893/) (2019)
- [GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2001.06216/) (2020)
- [Graph Structure of Neural Networks](https://mlsanity.com/paper/2007.06559/) (2020)
- [Spectral Graph Convolutions for Population-based Disease Prediction](https://mlsanity.com/paper/1703.03020/) (2017)
- [Graph Neural Network for Interpreting Task-fMRI Biomarkers](https://mlsanity.com/paper/1907.01661/) (2019)
- [InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/1903.04233/) (2019)
- [Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis](https://mlsanity.com/paper/2007.14589/) (2020)
- [Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy](https://mlsanity.com/paper/2006.14811/) (2020)
- [Multi-scale brain networks](https://mlsanity.com/paper/1608.08828/) (2016)
- [Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates](https://mlsanity.com/paper/2012.14131/) (2020)
- [Deep Representation Learning For Multimodal Brain Networks](https://mlsanity.com/paper/2007.09777/) (2020)
- [Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer](https://mlsanity.com/paper/2009.11166/) (2020)
- [Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint](https://mlsanity.com/paper/2009.13217/) (2020)
- [GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes](https://mlsanity.com/paper/2009.11080/) (2020)
- [Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data](https://mlsanity.com/paper/1708.00587/) (2017)
- [Learning to Segment Anatomical Structures Accurately from One Exemplar](https://mlsanity.com/paper/2007.03052/) (2020)

## Built on by (most cited first, 30 of 92)

- [BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks](https://mlsanity.com/paper/2204.07054/) (2022)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [Towards Effective and General Graph Unlearning via Mutual Evolution](https://mlsanity.com/paper/2401.11760/) (2024)
- [A Survey on Spectral Graph Neural Networks](https://mlsanity.com/paper/2302.05631/) (2023)
- [From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond](https://mlsanity.com/paper/2310.10121/) (2023)
- [AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity](https://mlsanity.com/paper/2401.11750/) (2024)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [coVariance Neural Networks](https://mlsanity.com/paper/2205.15856/) (2022)
- [GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis](https://mlsanity.com/paper/2203.09034/) (2022)
- [Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification](https://mlsanity.com/paper/2312.04111/) (2023)
- [Biologically Plausible Brain Graph Transformer](https://mlsanity.com/paper/2502.08958/) (2025)
- [Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks](https://mlsanity.com/paper/2211.11596/) (2022)
- [MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction](https://mlsanity.com/paper/2406.14455/) (2024)
- [OpenGU: A Comprehensive Benchmark for Graph Unlearning](https://mlsanity.com/paper/2501.02728/) (2025)
- [Predicting cognitive scores with graph neural networks through sample selection learning](https://mlsanity.com/paper/2106.09408/) (2021)
- [Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations](https://mlsanity.com/paper/2302.11354/) (2023)
- [MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning](https://mlsanity.com/paper/2312.14574/) (2023)
- [A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1](https://mlsanity.com/paper/2312.17506/) (2023)
- [Sparse Covariance Neural Networks](https://mlsanity.com/paper/2410.01669/) (2024)
- [A Topological Deep Learning Framework for Neural Spike Decoding](https://mlsanity.com/paper/2212.05037/) (2022)
- [Fast Temporal Wavelet Graph Neural Networks](https://mlsanity.com/paper/2302.08643/) (2023)
- [Fair CoVariance Neural Networks](https://mlsanity.com/paper/2409.08558/) (2024)
- [NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes](https://mlsanity.com/paper/2409.17510/) (2024)
- [Modern Views of Machine Learning for Precision Psychiatry](https://mlsanity.com/paper/2204.01607/) (2022)
- [Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence](https://mlsanity.com/paper/2206.03247/) (2022)
- [A Primer on Temporal Graph Learning](https://mlsanity.com/paper/2401.03988/) (2024)
- [Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping](https://mlsanity.com/paper/2204.05110/) (2022)
- [Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI](https://mlsanity.com/paper/2306.14080/) (2023)
- [Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates](https://mlsanity.com/paper/2110.03453/) (2021)

## Abstract (arXiv)

Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration. The list of papers cited in our work is available at https://github.com/basiralab/GNNs-in-Network-Neuroscience.