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 ...

Debate and discussion around data management, analytics, BI and information governance. This is a guest blogpost by Emil Eifrem, co-founder and CEO at Neo4j. He writes on why he thinks graph ...

The latest trends in software development from the Computer Weekly Application Developer Network. This is a guest post for the Computer Weekly Developer Network written by Philip Rathle in his role as ...