Graph neural networks (GNNs) are a type of neural network architecture and deep learning method that can help users analyze graphs, enabling them to make predictions based on the data described by a ...

Graph classification seeks to assign labels to entire graphs by extracting and learning from their structural patterns. Early approaches relied on graph kernels, which measure similarity by counting ...

A new open-access study reports that fusing attribute features with graph structure and training classifiers collaboratively ...

Efficiently and quickly chewing through one trillion edges of a complex graph is no longer in itself a standalone achievement, but doing so on a single node, albeit with some acceleration and ...