Graph neural networks help to process and analyze complex graph-structured data, unlocking new possibilities across a wide range of applications.

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.

Recently, Tianjin Yingjie Technology Development Co., Ltd. announced the application for a patent titled "An Optimization Method for Multi-Robot Collaborative Task Scheduling Based on Graph Neural ...

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 ...

Graph Neural Networks are on the path to becoming more mainstream, with exciting opportunities for the maturation of essential operations across message passing such as scatters, gathers, segmented ...

Graph neural networks operate on graph structures, as opposed to other types of neural networks that operate on vectors. What this means in practice is that they can leverage additional information.

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 ...

In most of these applications, neural networks outperformed traditional statistical models, such as discriminant and regression analysis. Furthermore, these applications have shown significant success ...

Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...

Earth Scientists have used machine learning for at least three decades and the applications span is large, from remote sensing to analysis of well log data, among many others. Although machine ...