Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

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 ...

Graph neural networks (GNNs), which model connections between things as pairwise connections, excel at inferring data that’s missing from large data sets, but as in other applications, they could miss ...

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...

Nanoengineers have developed new deep learning models that can accurately predict the properties of molecules and crystals. The models can enable researchers to rapidly scan the nearly-infinite ...

Nanoengineers at the University of California San Diego have developed new deep learning models that can accurately predict the properties of molecules and crystals. By enabling almost instantaneous ...

Graph theory and computational modeling reveal that neural network architecture biases the male Caenorhabditis elegans brain toward prioritized sexual behaviors.