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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 ...

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 ...

The last two years have delivered a new wave of deep learning architectures designed specifically for tackling both training and inference sides of neural networks. We have covered many of them ...

Modern businesses possess complicated networks of data, connecting information like customer behavior to marketing campaigns or fraud detection. But, to run useful AI predictions on the data often ...

The Optum Enterprise and Data Analytics (EDA) Graph & Health @ Scale (GHS) team is happy to announce v1.0 of our g2gnn library. In this presentation we will discuss the g2gnn library, how it works, ...

Graph machine learning (or graph model), represented by graph neural networks, employs machine learning (especially deep learning) to graph data and is an important research direction in the ...

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 ...