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 ...

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 ...

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 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 ...

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

Katie Roberts, PhD, data science solution architect at Neo4j, joined DBTA's webinar, 'Solving Data Challenges with Knowledge Graphs and Context-Aware Recommendation Systems,' to explore how building ...

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.