A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

Python Machine Learning Toolbox for Brain Network Classification. Source codes are included of the top 20 teams in the Kaggle competition.

Deep hypergraph U-Net (HUNet) for brain graph embedding and classification.

Multi-View LEArning-based data Proliferator (MV-LEAP) for boosting classification using highly imbalanced classes.

FS-Select identifies the best feature selection (FS) method for a given dataset from a pool of FS methods.

We provide both Matlab and Python versions of netNorm. In this folder you find the Maltab version of the code.

Awesome graph neural networks for brain network learning. Collections of related research papers with implementations, commonly used datasets and tools. We also invite researchers interested in brain graph learning with GNNs to join the project.

netNorm (network normalization) framework for multi-view network integration (or fusion), recoded up in Python by Ahmed Nebli.

Supplementary code for the paper: Incomplete annotation has a disproportionate impact on our understanding of Mendelian and complex neurogenetic disorders

Graph registration network using representative templates

Epilepsey and Insomnia Detection Using EEG signals