As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

Identity management for cybersecurity is inherently a complex graph problem due to the vast, dynamic, and interconnected nature of modern IT environments. The Fast Company Executive Board is a private ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

The foundation for Knowledge Graphs and AI lies in the facets of semantic technology provided by AllegroGraph and Allegro CL. AllegroGraph is a graph based platform that enables businesses to extract ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

PORTLAND, Ore.--(BUSINESS WIRE)--thatDot, Inc., a pioneer in complex event stream processing software, today released Quine. Quine’s unique approach combines graph data and streaming technologies into ...

When Daimler Truck Holding AG began the long and complex process of separating from Mercedes-Benz Group AG in 2021, it faced a daunting problem. Decades of tightly interwoven information technology ...

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.