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02-12-2020 04:55 AM
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
02-13-2020 04:38 AM
Thanks for your work on this project. This seems like a big step in maturity.
02-13-2020 06:37 AM
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!
02-17-2020 11:21 AM
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
02-18-2020 12:34 PM
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.
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