A working example project called GraphSAGE-GraphRAG demonstrates that structure-aware retrieval using a Neo4j knowledge graph and an offline-trained GraphSAGE GNN can solve multi-hop reasoning that fl…
Neo4j's Sr. Manager of Technical Product Marketing explains GraphSAGE, an inductive graph embedding model that generates embeddings for new nodes without retraining, using a three-step process of samp…
Neo4j's Aura Graph Analytics now supports GraphSAGE, an inductive node embedding method that generates embeddings for unseen nodes without retraining, addressing the cold-start problem in growing grap…
Fraud rings evade traditional machine learning models like XGBoost because they analyze individual rows rather than relationships between entities. Graph Neural Networks (GNNs) detect coordinated frau…
A new coding implementation demonstrates how to infer urban functions using spatial graph neural networks. The pipeline collects POI and street network data from OpenStreetMap, constructs proximity gr…
Researchers have developed RelGT-AC, a relational graph transformer designed to predict missing column values in relational databases, functioning as an intelligent form-filling assistant. The model i…