Train once, embed forever: GraphSAGE and the model repository in Aura Graph Analytics

GraphSAGE in Neo4j Aura Graph Analytics addresses the cold-start limitation of transductive embedding methods such as Node2Vec and FastRP by learning an inductive function that can generate embeddings for previously unseen nodes using their features and graph neighborhoods. The post demonstrates this approach with a synthetic e-commerce graph containing customers, products, purchases, and referrals, training a multi-label GraphSAGE model with customer and product properties projected into a shared feature space. The trained model is stored in Aura Graph Analytics’ model catalog, allowing it to be retrieved and applied without retraining when 50 new customers and their initial purchases are added. Embeddings for both existing and new customers are then written to AuraDB and used in a filtered K-nearest-neighbors similarity search that matches new customers with comparable established customers. The example illustrates a workflow in which models are retrained periodically while embeddings for incoming entities can be generated on demand, supporting growing graphs and recommendation-related use cases.

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