With artificial neural networks becoming more popular and capable, GNNs have become a powerful tool for many important applications.

Google today released TensorFlow Graph Neural Networks (TF-GNN) in alpha, a library designed to make it easier to work with graph structured data using TensorFlow, its machine learning framework.

Research team introduced Soft-GNN, a framework to train robust GNNs under noisy conditions. Soft-GNN mitigates label noise impact through dynamic data selection, achieving better performance and ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.

Computer scientists have written a network flow algorithm that computes almost as fast as is mathematically possible. This algorithm computes the maximum traffic flow with minimum transport costs ...

We applied three types of established GNN techniques, namely Crystal Graph Convolutional Neural Network (CGCNN), Materials Graph Network (MEGNET), and Atomistic Line Graph Neural Network (ALIGNN), to ...

As a result, this quantum neural network can naturally achieve better generalization without requiring additional regularization techniques.