Most examinations of neural networks’ learned latent spaces typically employ dimensionality reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE) or uniform manifold ...

Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, and recommendation systems). In this vein, ...