An experimentation framework that assesses how well graph neural networks (GNN) can minimize various attributed graph functions on the node domain. read more of Universal Minimization on the Node Domain

A novel progressive training algorithm for learning Graph Neural Networks with Differential Privacy guarantees read more of ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

A tailored Graph Neural Network architecture with Differential Privacy guarantees for both training and inference. read more of GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

A graph neural network framework on tensorflow. read more of gnn-tf

Graph Neural Networks on Native Java read more of JGNN

Federated training of Graph Neural Networks (GNNs) with Local Differential Privacy read more of Locally Private Graph Neural Networks