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.

The semantic technologies underpinning RDF knowledge graphs are primed for data mesh and data fabric architectures — and their synthesis. They’re certainly ideal for crafting data products.

Graph analytics is a set of analytic techniques that shows how entities such as people, places and things are related to each other. Unlike traditional data analytics, which is slow and unable to ...

Know How Your Data and Color Convey Information. A graph's success depends in part on if it creates a cognitive load for the audience. Cognitive load refers to the amount of information a brain ...

SHACL allows a data graph, for instance, to specify the corresponding shapes graph used to describe the link between a given shape and targeted data. Franz claims its upgraded, flexible architecture ...

Both bar and line graphs share a weakness in that they display only the mean values commonly employed to summarize datasets, rather than the dataset values themselves. 6 For example, using a bar graph ...

A Knowledge Graph is a connected graph of data and associated metadata applied to model, integrate and access an organization’s information assets. The knowledge graph represents real-world entities, ...

July 10, 2025) - The Graph, the open, universal data layer for web3, announced today a strategic integration with the TRON ...

As data complexity continues to grow and the demand for real-time insights increases, the move away from traditional relational databases and towards the adoption of graph databases will become vital.