Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

Tufin will showcase these exciting new agents along with its other TufinAI-powered capabilities and its vision for Multi-Vendor Agentic Network Security, at the upcoming RSA Conference 2026 (RSAC), ...

You wouldn’t change up your entire production process based on sales from just a couple of locations, and you wouldn’t lower auto insurance premiums across the board because collision rates went down ...

Graphs have been around forever, but the internet has given them new life. It's refocused our attention on the use of graph concepts for information search as an option to traditional hierarchical ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Graphs are, quite simply, a universal ...

A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, ...

One theme cuts across the most credible 2026 predictions: autonomy will be driven less by bigger models and more by better ...

Nanoengineers have developed new deep learning models that can accurately predict the properties of molecules and crystals. The models can enable researchers to rapidly scan the nearly-infinite ...

Accurate stock trend forecasting is a central challenge in financial economics due to the highly nonlinear and interdependent nature of market dynamics. Traditional statistical and machine learning ...