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.

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful.

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 ...

Graph data science helps solve problems from fraud to personalization and drug repurposing in various industries. Visualized in Neo4j Bloom. Image courtesy of Neo4j. Neo4j for Graph Data Science ...

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 ...

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 ...

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 ...

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks.

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

Data science is “a growing segment of our enterprise customers that made up about 30% of new customers last quarter,” said Alicia Frame, senior director of graph data science at Neo4j.