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.

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.

The graph database is popular with social networks, but there's no reason to limit it to tracking people and their friendships.

AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast ...

We are each nodes in a network, connected to many other people, projects, information, and history. [Graph databases] are vastly more powerful for modeling how people work in networks, how people ...

Banks, miners and police forces in Australia are among those using graph databases to provide the context and data relationships needed for more accurate and trustworthy AI, moving projects from ...

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 enable investigators to store detailed patterns of problematic actors. Then, they can query the data to uncover intricate connections between the suspicious actor and other entities.

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 ...