I'll try to help:
First off: the Neo4j Link prediction in the ML Models catalog (Link Prediction - Neo4j Graph Data Science) deploys a logistic regression algorithm. The LightGCN, a stripped-down GCN model, still relies on embeddings. To the best of my knowledge, there has not been a benchmarking exercise comparing both models.
However, for recommendations, the GraphSage model is often used and indeed proven to be very effective (GraphSAGE - Neo4j Graph Data Science). This allows to compute a vector representation of the node and it's neighborhood after which you can release a bunch of similarity algo's from the Neo4j GDS library to compare and classify vectors. Also, the GraphSage model is inductive; once trained you can 'estimate'/ predict vectors for any new node that is added to the graph, and subsequently find similar vectors such as the predicted one.
Should you still want to do some more specific modeling then you can deploy the Neo4j Python drivers and use one of the Deep Graph Library models and neural network engines and populate your Neo4j Graph from your local environment, say a Colab notebook, like shown in this article:
Hope this helps, and happy modeling!