Basically, doing data science with Neo4j data has been painful, expensive, and not scalable, according to Neo4j’s lead product manager Alicia Frame. But that all should change with today’s launch of ...

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.

Auer is a Data Science professor with many contributions in Knowledge Graph research and is leading ORKG. Dr. Auer identified two key issues in scientific research.

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 ...

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 ...

This is a guest blogpost by Emil Eifrem, CEO, Neo4j. In his view, four major trends driving interest in graph technology are starting to surface. Let's look ahead to what could be in store for the ...

Graph analytics is an ideal technology to help to tackle the challenges caused by large, disparate, datasets since it becomes more impactful as the volume, velocity and variety of data expands. [2] ...

Graphs are among the most widely-used data structures in machine learning. Their power comes from the flexibility of capturing relations (edges) of collections of entities (nodes) which arise in a ...