There are 5 repositories under network-neuroscience topic.
A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.
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.
HADA (Hiearachical Adversarial Domain Alignment) for brain graph prediction and classification.
Predicting multigraph brain population from a single graph
Federating temporally-varying graph timeseries
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
We provide both Matlab and Python versions of netNorm. In this folder you find the Maltab version of the code.
netNorm (network normalization) framework for multi-view network integration (or fusion), recoded up in Python by Ahmed Nebli.
Methods for estimating time-varying functional connectivity (TVFC)
Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs? (Python3 version)
Recurrent multigraph neural network
SM-NetFusion for supervised multi-topology network cross-diffusion.
Supervised graph diffusion and fusion.
Non-isomorphic Inter-modality Graph Alignment and Synthesis.
NAG-FS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification.
Residual Embedding Similarity-based Network Selection (RESNets) for forecasting network dynamics.
One-representative shot learning for graph classification.
Benchmarks for functional connectivity estimators and FCEst Python package
The python implementation of the Network Noise Rejection method for community detection in undirected networks. Find the origin matlab version here: https://github.com/mdhumphries/NetworkNoiseRejection
Learning-guided Graph Dual Adversarial Domain Alignment (LG-DADA) framework for predicting a target graph from a source graph.
NAGFS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification code, recoded by Dogu Can ELCI.
Detect and analyze Network Motifs of any network, with Python.
LaTeX code for my PhD thesis.
Multi-Modal Dynamical Coherence Analysis Toolbox
Code to identify hubs through degree of nodes, given adjacency matrices of a population sample
Publicly available code for "A flexible hub connectivity mechanism for cognitive control". Manuscript in-preparation. Part of C. Cocuzza's dissertation (Aim 3).