Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

Graph neural networks (GNNs) are powerful artificial intelligence (AI) models designed for analyzing complex, unstructured graph data. In such data, entities are represented as nodes and ...

Graph analytics databases have been described as embodying the next generation of data storage connected with AI, and that's what innovation is all about: a new-and-improved version of how ...

Property graphs such as Neo4j or TigerGraph are extremely popular for building out knowledge graphs. Neo4j is particularly well regarded by developers as easy to get started with, while TigerGraph ...

Data is a collection of facts in a raw or unorganized form, such as numbers or characters. Without context, data does not mean much. For example, "18122020" is just a sequence of numbers.

As the graph data model is well understood and also shared with many other solutions, let’s examine the actor model and standing queries. Computation in Quine is built on the Actor Model using Akka.

Neo4j, which offers a graph-centric database and related products, announced today that it raised $325 million at a more than $2 billion valuation in a Series F deal led by Eurazeo, with ...