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

Regression Graph Neural Network (regGNN) for cognitive score prediction.

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

Graph SuperResolution Network using geometric deep learning.

Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks

HADA (Hiearachical Adversarial Domain Alignment) for brain graph prediction and classification.

Predicting multigraph brain population from a single graph

MGN-Net: A novel Graph Neural Network for integrating heterogenous graph population derived from multiple sources.

Multigraph fusion and classification network using graph neural network

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

Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data

Federating temporally-varying graph timeseries

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

Methods for estimating time-varying functional connectivity (TVFC)

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

Supervised graph diffusion and fusion.

Recurrent multigraph neural network

SM-NetFusion for supervised multi-topology network cross-diffusion.

Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs? (Python3 version)

Federated Multimodal and Multiresolution Graph Integration