Graph databases are making a splash in the database market, with specialist, multimodal and cloud database suppliers jostling for a slice of the pie.

[Graph databases] are vastly more powerful for modeling how people work in networks, how people search for data and objects, how people communicate and build different types of relationships (peers, ...

New techniques make graph databases a powerful tool for grounding large language models in private data.

Dr. Alin Deutsch of UC San Diego explains in a Q&A why graph database algorithms will become the driving force behind the next generation of AI and machine learning apps.

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.

Graph databases are increasingly popular. In fact, according to DB-Engines graphs are the fastest growing of any database category since 2013. This growth is fueled in part because many organizations ...

Aerospike is expanding its database capabilities with its Aerospike Graph database, which brings graph data model capabilities.

TigerGraph Inc. has launched its managed graph database on Google Cloud, enabling customers of the search giant’s infrastructure-as-a-service platform to use the software in their analytics ...

A hitherto under the radar graph database that uses blockchain to support data lineage and verification wants to take over the world, starting with the US Department of Defense ...

Fraud detection and identity authentication are the two main use cases that Aerospike sees customers using the graph database to build. Fraud detection, where connections to known fraudulent entities ...