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 four pillars of graph adoption This confluence of graph analytics, graph databases, graph data science, machine learning, and knowledge graphs is what makes graph a foundational technology.

Graph data science is an emerging field with a lot of promise, but it’s being hamstrung by the need for practitioners to have lots of data engineering and ETL skills. Now Neo4j is hoping to drive that ...

To this end, Redwood City, Calif.-based graph analytics provider TigerGraph has enlisted as chief scientist a thought leader in this sector, Dr. Alin Deutsche, a data science professor at UC San ...

Graph analytics can be performed on any back end, as they only require reading graph-shaped data. Graph databases are databases with the ability to fully support both read and write, utilizing a ...

Data scientists have been involved with Neo4j, but mostly on the periphery, Frame says. “They see the value of Neo4j,” she says. “But so far what they were mostly able to do … is store their data in ...

Graph analytics mainly includes graph processing, graph mining and graph learning, and is very widely used in practical applications. As the amount of graph data continues to expand, graph ...

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 ...

That’s for the data storage folks. For the data scientists, the company offers Neo4j Graph Data Science Library, a set of comprehensive tools for analyzing graph data.