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.

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 Graph Data Science makes it easy for data scientists to work within their existing data pipeline of tools across their ecosystem. Data scientists can use Neo4j Graph Data Science on-premises, ...

Neo4j is among the graph database vendors who have been around the longest, and it now is the best-funded one, too. But that does not mean it's the only one worth keeping an eye on. Amazon entered ...

Neo4j, a provider of graph technology, is launching Neo4j for Graph Data Science, a data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j ...

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.

You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.

The COVID GRAPH and Open Research Knowledge Graph (ORKG) teams have focused on COVID-19, and emphasized annotation and structure, respectively. Connected Papers seems to expand coverage, and ...

The goal is to design a graph neural network based recommendation system for scroll paintings (folk art) that can run on decentralized compute environments (such as peer-to-peer systems). Requires ...