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

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.

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 ...

By learning the relevant features of clinical images along with the relationships between them, the neural network can ...

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 ...

Using this information, the model can then tell us the probability of a drug-protein interaction that we did not previously ...

GNNs extend the foundational ideas of Convolutional Neural Networks (CNNs) to graph data. While CNNs capture spatial locality in grid-like data (for example, images) through convolutional kernels, ...

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 ...