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 ...

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ...

Franz Inc., an early innovator in AI and leading supplier of graph database technology, is releasing AllegroGraph 7.2, providing organizations with essential data fabric tools, including graph neural ...

A new framework integrates graph databases with real-time machine learning to enhance fraud detection and risk control in digital finance. By ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

Traditional experimental methods for evaluating gas adsorption performance of metal–organic frameworks (MOFs) are costly and time-consuming, while ...

Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...

Artificial digital neural network concept. Neural network software enables the implementation, deployment and training of artificial neural networks. These networks are designed to mimic the behavior ...

How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in ...