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 ...

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.

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 ...

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 ...