Graph Embeddings, One Line of Code Away

A single account on its own tells you very little. But look at who it sends money to, who those accounts send money to, and suddenly you have got a lot to work with. That’s the idea behind graph embeddings: take a node and everything around it, and compress that graph structure into a vector. Nodes with similar surroundings end up with similar vectors. You will also see these called node embeddings, the name we use in the docs, because every node gets a vector of its own. Over the last few years, embeddings have been the fastest-growing use case for Neo4j Graph Data Science (GDS), our graph analytics and machine learning library, and…