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• We’ve just released a preview of Neo4j’s Graph Data Science™ Library version 1.4, which includes graph embeddings and an ML model catalog. Together, these enable you to create representations of your graph and make graph predictions – all within Neo4j.

• Neo4j for Graph Data Science™ is the first and only commercially available graph-native ML functionality for enterprises, and we’re immensely happy to help democratize these innovations. This release enables anyone to employ cutting edge ML techniques, which will change the way enterprises use their connected data.

• In Neo4j, the k-NN algorithm can be used to create edges between nodes based on similar embeddings. Alternatively, Neo4j’s Python driver can also pull embeddings into an existing workflow.

We’re delighted to announce you can now take advantage of graph-native machine learning (ML) inside of Neo4j! We’ve just released a preview of Neo4j’s Graph Data Science™ Library version 1.4, which includes graph embeddings and an ML model catalog.

We’ve just released a preview of Neo4j’s Graph Data Science™ Library version 1.4, which includes graph embeddings and an ML model catalog. Together, these enable you to create representations of your graph and make graph predictions – all within Neo4j.

Neo4j for Graph Data Science™ is the first and only commercially available graph-native ML functionality for enterprises, and we’re immensely happy to help democratize these innovations. This release enables anyone to employ cutting edge ML techniques, which will change the way enterprises use their connected data.

In Neo4j, the k-NN algorithm can be used to create edges between nodes based on similar embeddings. Alternatively, Neo4j’s Python driver can also pull embeddings into an existing workflow.