Gilead Sciences uses graph neural networks and graph databases to reveal hidden fraud networks and ensure patient's safety.

Mariia Bulycheva discusses the transition from classic deep learning to GNNs for Zalando's landing page. She explains the complexities of converting user logs into heterogeneous graphs, the "message ...

Graph neural networks (GNNs) have emerged as a versatile class of machine-learning models designed to process data structured as graphs, capturing relationships among entities through iterative ...

The proposed framework integrates LSTM and GNN models to predict traffic anomalies and collision risks in V2X systems. By ...

AI solution Our team is offering a powerful solution, which marries deep learning with fundamental physics: the Physics-Informed Graph Neural Network (PIGNN). This framework represents a device’s mesh ...

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...