Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Graphs are everywhere around us. Your social network is a graph of ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Graph theory isn’t enough. The mathematical language for talking ... These are, effectively, hypergraphs viewed through the framework of topology. Neural networks, which fall into the general category ...

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 ...

By integrating Monte Carlo/Molecular Dynamics simulations to predict surface segregation with a graph neural network (GNN) to assess site-specific activity, this approach establishes a crucial ...

“If we look at what’s happening today, there are some amazing technologies coming up,” Josifovski said, such as graph neural networks, transformer models, and language models. Graph neural ...

But as mobile hardware advances, Machine Learning (ML) techniques, particularly Graph Neural Networks (GNNs), are emerging as a powerful, efficient alternative to emulate physics on mobile. GNNs are ...

Besides computer science, this type of optimization problem has implications for modeling the precise networks of chemical ... Borgs's prior university labors on graph theory and phase transitions ...