For a long time, companies have been using relational databases (DB) to manage data. However, with the increasing use of ...

Soo Kim announced on the 8th of September that the team has developed a new DB system named 'Chimera' that fully integrates ...

AI and graphs have a few things in common: they are multi-faceted, ubiquitous in their applications, and seeing rapid growth in the 2020s.

The four pillars of graph adoption This confluence of graph analytics, graph databases, graph data science, machine learning, and knowledge graphs is what makes graph a foundational technology.

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

For me, the flexibility of graph technology is what makes it so valuable for data modeling. Having products based on graph analytics is nothing new – Google and Facebook are both based on graphs.

Scene graphs allow humans and machines to categorize and query images based on complex relationships among objects in a scene.

Automotive giant Daimler is using Neo4j's graph database technology in its HR department. ZDNet spoke to Jochen Linkohr, the manager of HR IT at Daimler, to find out more.

A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...

Graph technology works by connecting different sources of data to reveal real-world connections, relationships, and patterns to unlock greater value and help inform decisions.

Swiss bankers are notorious for their secrecy, but when Swiss bank account data is leaked, it takes technology to help unravel the complex relationships.

For graph technology to have its largest impact, analysts, developers, and business executives must be able to think in graphs the way Graham and his team at Viaweb thought in Lisp.