Here we use Graph Neural Networks (GNNs) to learn a message-passing algorithm that solves these inference tasks. We first show that the architecture of GNNs is well-matched to inference tasks. We then ...

By integrating Monte Carlo/Molecular Dynamics simulations to predict surface segregation with a graph neural network (GNN) to assess site-specific activity, this approach establishes a crucial ...

Abstract: Graph convolutional network (GCN) has garnered significant attention in hyperspectral image (HSI) classification due to their ability to model non-Euclidean structured data. Compared with ...

A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...