Topological Deep Learning

Investigating the reproducibility of federated GNN models

Predicting multigraph brain population from a single graph

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.

Introduction to simple machine learning, deep learning and geometric deep learning concepts and methods.

AI Toolkit for Healthcare Imaging

Comparative survey of multigraph integration methods

Non-isomorphic Inter-modality Graph Alignment and Synthesis.

Graph Neural Network Library for PyTorch

Multigraph fusion and classification network using graph neural network

Recurrent multigraph neural network

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

Recurrent Dynamic Graph Mapper using GNN

One-representative shot learning for graph classification.

Reproducible generative learning

A few-shot learning approach to forecasting the evolution of the brain connectome.

StairwayGraphNet for inter- and inter-modality graph superresolution.

Recurrent multigraph integrator network using graph neural network.

L2S-KDNet for super-resolving brain graphs using teacher-student network

Predicting the multi-trajectory evolution of multimodal brain connectivity.

A Python toolbox for predicting brain network (graph) evolution over time from a single observation. The codes of the 20 competing Kaggle teams along with the competition datasets are made available.

Federated multigraph integration with application to connectional brain template estimation.

AI Toolkit for Healthcare Imaging

Intermodalitty graph superresolution.

The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML

Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data

HCAE (HyperConnectome AutoEncoder) for brain state identification.