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.

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

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.

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 ...

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 ...

AWS is launching a serverless flavor of its Neptune graph database service that abstracts away managing compute capacity.

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

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. A quantum ...