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 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 ...

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.

AI, for both mobile and fixed solutions, announced that is now working on Graph Neural Network (GNN) based autonomous machine algorithms development, as part of the research to enhance its Avant! AI.

In this paper, we introduce SCAR, a novel pre-silicon power side-channel analysis framework based on Graph Neural Networks (GNN). SCAR converts register-transfer level (RTL) designs of encryption ...

This will help Flipkart and FITT to develop a general-purpose user activity graph that can be used for different purposes using the GNN (graph neural network) based techniques.