Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Graph matching remains a core challenge in computer vision, where establishing correspondences between features is crucial for tasks such as object recognition, 3D reconstruction and scene ...

A research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. A KAIST research team has developed a new ...

You wouldn’t change up your entire production process based on sales from just a couple of locations, and you wouldn’t lower auto insurance premiums across the board because collision rates went down ...