Graph Data Science Library Preview

[alicia_frame]

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 https://neo4j.com/docs/graph-data-science/1.0-preview/, 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: https://github.com/neo4j/graph-data-science/issues

[nsmith_piano]

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

[doulkifli]

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!

[okmich]

Thanks for the work on this project.

Question:
Please clarify how this project differ from the existing graph algorithm work going on in https://github.com/neo4j-contrib/neo4j-graph-algorithms for graph algorithms.

Thanks

[alicia_frame]

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 https://github.com/neo4j-contrib/neo4j-graph-algorithms repo.