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.

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

Graph database query languages are growing, along with graph databases. They let developers ask complex questions and find relationships.

Lyft's "Amundsen" metadata system is an example of how knowledge graphs are spreading throughout companies with grass-roots projects. It's all part of winning hearts and minds, in the view of ...

Companies that want to use powerful graph algorithms to explore hidden connections in their data may want to check out TigerGraph, which today unveiled a pair of cloud-based offerings designed to ...

Ever since the introduction of the Google Knowledge Graph, a growing number of organizations have adopted this powerful technology to drive efficiency and effectiveness in their data management ...

Graph database startup Neo4j raised $320 million at an over $2 billion valuation, highlighting the value of graph databases.

Neo4j has announced the general availability of the next generation of its namesake graph database for both community and enterprise users. Neo4j 5 widens the performance lead of native graphs over ...

DZD, the German Federal Diabetes Research Centre, is using a Neo4j graph database to link up Covid-19 scientific research and scientists.

But the Microsoft Graph and LinkedIn aren’t Microsoft’s only graphs with APIs: Dynamics 365 has the Common Data Service, a way of describing standard items in a business.