Understanding graph embeddings with Neo4j and Emblaze

Graph embeddings can represent the rich network of relationships and properties in a graph as vectors. These embedding vectors are useful for comparing nodes, and they are also valuable inputs for machine learning algorithms. Neo4j Graph Data Science makes it possible to derive embeddings from a graph using only a few lines of Python code.

While it’s pretty simple to generate embeddings with Neo4j, it’s not always easy to tell if you have the right embedding for your application. Neo4j …

data science dimensionality-reduction embedding graph neo4j understanding