Graph neural networks are very powerful tools. They have already found powerful applications in domains such as route planning, fraud detection, network optimization, and drug research.

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 ...

By learning the relevant features of clinical images along with the relationships between them, the neural network can ...

Other than giving us an appreciation how little difference going eight miles an hour over the speed limit makes, that ETA service is a remarkable invention — and one that takes a hell of a lot of ...

Google's new Graph Foundation Model delivers up to 40 times greater precision and has been tested at scale on spam detection.

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at ...

A team of chemistry, life science, and AI researchers are using graph neural networks to identify molecules and predict smells. Models made by researchers outperform current state-of-the-art ...

Franz Inc., an early innovator in AI and leading supplier of graph database technology, is releasing AllegroGraph 7.2, providing organizations with essential data fabric tools, including graph neural ...