Release Highlights: Neo4j Graph Data Science 2.3
Learn all about what's new in Neo4j Graph Data Science 2.3! Join Zach Blumenfeld as he walks you through how to:
Perform fast and scalable Graph ML using the Knowledge Graph Embedding - HashGNN
Find the shortest, least expensive route to a location from multiple starting points using the Steiner Tree pathfinding algorithm
Incorporate your negative examples to train link prediction models faster.
Transform directed relationships into undirected relationships
And more!
Check out the resources below if you want to learn more
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Resources
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Notebooks / Demos / Technical Resources
Graph construct dataframe to graph example, improving ML with graph embeddings
Graph Data Science 2.3 Links
Graph Data Science release announcement
What’s new summary
Graph Data Science
Steiner trees
HashGNN
Resources mentioned
Getting Started with Graph Data Science
Python Client
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