Graph Data Science Library Preview

We’re excited to share a preview release of the graph data science (GDS) library -- currently available on the Neo4j download center. The docs are currently up at, and the code has been open sourced on our github repo. For more details on what's in the library, check out our release notes for a summary.

This library includes all of your favorite algorithms from the graph algorithms library - as well as some new ones! - plus a new, unified, simplified surface, improvements to the graph loaders, improved error messaging, and additional features and workflows to support production scale deployments.

If you’re interested in a quick start guide to the GDS, we’ve created a browser guide to walk you through the basics -- just install the plugin and enter :play graph-data-science in neo4j browser.

If you have any feedback, please open an issue on github: Issues · neo4j/graph-data-science · GitHub


Thanks for your work on this project. This seems like a big step in maturity.

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Thanks for sharing. I am about to launch and supervise a student final-year project about the use of graph algorithms on Question/Answer systems (e.g. StackExchange) in order to extract uselful knowledge about users, posts, tags. Neo4j's algorithms would certainly be of a great help!


Thanks for the work on this project.

Please clarify how this project differ from the existing graph algorithm work going on in GitHub - neo4j-contrib/neo4j-graph-algorithms: Efficient Graph Algorithms for Neo4j for graph algorithms.


The graph data science library is a successor to the graph algorithms repo, actively maintained and supported by the neo4j product engineering team (versus the developer relations team). There is no ongoing work in the repo.

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