Graph databases work best when the data you’re working with is highly connected and should be represented by how it links or refers to other data, typically by way of many-to-many relationships.

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.

A new generation of graph databases has taken hold, and a generation of query languages has arrived alongside them. The assorted graph database query languages include the likes of Gremlin, Cypher ...

Startups like TigerGraph, MongoDB, Cambridge Semantics, DataStax, and others compete with Neo4j in a graph database market expected to be worth $2.4 billion by 2023, in addition to incumbents like ...

AWS makes Neptune, its graph database service, serverless Kyle Wiggers 9:13 AM PDT · October 27, 2022 ...

At Data Summit Connect 2020, Thomas Cook, director of sales, Cambridge Semantics, explained the basics of knowledge graphs and how they leverage natural-language processing to automate the ...

Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade.

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial ...

In a complete graph (left) every node is connected to every other. For other well studied graphs, the Paley graph in the center and the Latin square graph on the right, that is not true.