Neo4j has announced the availability of Neo4j for Graph Data Science, a data science environment that helps data scientists utilize predictive relationships and network structures to answer questions. It combines a native graph analytics workspace and graph database with graph algorithms and graph visualization. The workspace enables native graph creation and persistence for shaping in-memory graphs, with graph visuals in Neo4j Bloom helping teams to explore results quickly.
Neo4j offers a graph database that helps organizations make sense of their data by revealing how people, processes and systems are related. Neo4j natively stores interconnected data so it’s easier to decipher data. The property graph model also makes it easier for organizations to evolve machine learning and AI models. The platform supports high-performance graph queries on large datasets as well.