Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.

One technology experiencing a kind of renaissance is the graph database, which has existed for years but is finding new relevance in current cybersecurity contexts.

We talk with Daniel Kirstenpfad, founder and CTO of sones GmbH, about Graph Databases and how they can better model some types of data such as relations in a social networking application. A graph ...

With the rapid development of artificial intelligence (AI) technology, the graph database market is experiencing unprecedented growth, with an annual growth rate approaching 25%. Graph databases are ...

As graph database adoption accelerates, new data infrastructures will emerge to eliminate many of the scale struggles of graph database models.

George Anadiotis, Linked Data Orchestration/ZDNet: “2018 was the Year of the Graph, the year graph databases went mainstream. I have no reason to think this will change, it will only accelerate.

A lot of the graph use cases are really graph database use cases, so it could be that is what people are using. There's Neo4j, Titan -- now acquired by DataStax -- and Gaffer.

Interest in graph databases has exploded during the first five months of the year, as the product category threatens to lap other database types in popularity, according to figures from DB-Engines.com ...

Neo4j, the company behind the open source graph database, has launched a completely free version of its fully-managed cloud service.