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 application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.

But Neptune also exemplifies another important development in graph databases: integration of data science and machine learning features.

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

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 ...

Neo4j®, the leading graph database and analytics platform, today unveiled Infinigraph: a new distributed graph architecture now available in ...

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.

Neo4j for Graph Data Science will help us to identify where we need to direct biomedical research, resources, and efforts." Neo4j continues to be something of a harbinger of the growing need for ...

Enterprises that want to use powerful graph algorithms to discover relationships hidden in their data now have an easier path to get there thanks to the new data science library unveiled today by ...

Neo4j, a leading graph data platform, is unveiling Neo4j Graph Data Science, the company's comprehensive graph analytics workspace built for data scientists. The platform is now available with new and ...