What’s New with Graph Data Science: GDS 1.7 Release
Blog post from Neo4j
The latest release of Neo4j's Graph Data Science (GDS) focuses on making graph data science accessible, easy, and foolproof. It introduces machine learning pipelines for graph native link prediction, allowing users to define the steps they want to take to build their predictive model without worrying about data manipulation and math. This feature enables users to create a pipeline, define node and link features, split the data, and select the best-performing model, while Neo4j takes care of assembling those steps in the right order. The release also includes progress logging and system monitoring to make model building easier and less error-prone, as well as three new features requested by users: Cypher on the GDS Graph, string support for graph export, and graph partitioning.