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 ...

Here are a few applications for graph neural networks: Node classification: One of the powerful applications of GNNs is adding new information to nodes or filling gaps where information is missing.

Google's new Graph Foundation Model delivers up to 40 times greater precision and has been tested at scale on spam detection.

Other than giving us an appreciation how little difference going eight miles an hour over the speed limit makes, that ETA service is a remarkable invention — and one that takes a hell of a lot of ...

Graph neural networks (GNNs) are a relatively recent development in the field of machine learning. Like traditional graphs, a core principle of GNNs is that they model the dependencies and ...

On a “frame of reference” classification level, all methods are roughly divided into two categories: Eulerian (grid-based) and Lagrangian ... we adapt their architecture to TensorFlow 2, exploring the ...

To overcome such inherent challenges with graph neural networks and improve recommendation abilities, LinkedIn has created a process it calls Performance-Adaptive Sampling Strategy (PASS). that ...