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

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

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

Predicting multigraph brain population from a single graph

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

Federating temporally-varying graph timeseries

Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data

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.

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

Recurrent multigraph neural network

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

Federated Multimodal and Multiresolution Graph Integration