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.

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 ...

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

In a white paper, researchers at Bloomberg modeled supply chain data as a graph and used GNNs to create a long-short portfolio. The results demonstrate an edge over traditional approaches.

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

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 ...