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.

But following today’s announcement of a $325-million funding round that values the graph database company at over $2 billion, Neo4j expects to see more widespread use of graph algorithms for data ...

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 ...

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 ...

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 ...

Neo4j for Graph Data Science was conceived for this purpose – to improve the predictive accuracy of machine learning, or answer previously unanswerable analytics questions, using the ...

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