In the neo4j documentation, I saw that the node classification algorithm uses Logistic Regression as a classifier. How does it take node embeddings as a feature? Does it take it as a single feature or n (no. dimensions of embedding ) number of features?
If you look at the syntax section (Node Classification - Neo4j Graph Data Science), you can see the
featureProperties parameter. This is used to specify a list of node properties (multiple features).
One of these features could be an embedding.
Note you only specify the
featureProperties when training your model via
gds.alpha.ml.nodeClassification.train. On prediction, we assume there are exactly the same properties on the nodes as specified during train.