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.

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

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

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 ...

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 ...

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 ...

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 ...