A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.
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Updated
Feb 27, 2023
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.
MGN-Net: A novel Graph Neural Network for integrating heterogenous graph population derived from multiple sources.
Deep hypergraph U-Net (HUNet) for brain graph embedding and classification.
Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks
Multi-View LEArning-based data Proliferator (MV-LEAP) for boosting classification using highly imbalanced classes.
Residual Embedding Similarity-based Network Selection (RESNets) for forecasting network dynamics.
Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data
Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs? (Python3 version)
NAGFS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification code, recoded by Dogu Can ELCI.
SM-NetFusion for supervised multi-topology network cross-diffusion.
Federating temporally-varying graph timeseries
netNorm (network normalization) framework for multi-view network integration (or fusion), recoded up in Python by Ahmed Nebli.
Predicting multigraph brain population from a single graph
HADA (Hiearachical Adversarial Domain Alignment) for brain graph prediction and classification.
Supervised graph diffusion and fusion.
Heterogeneous federated learning for graph super-resolution
Recurrent multigraph neural network
Non-isomorphic Inter-modality Graph Alignment and Synthesis.
Multi-Modal Dynamical Coherence Analysis Toolbox
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