FASTRP GRAPH EMBEDDINGS WITH APACHE DATAFUSION

Summary from the TLDR Data newsletter

Graph embeddings turn connected entities into vectors for similarity search, GraphRAG, and graph machine learning, but Node2Vec's random walks create huge intermediate datasets and scale poorly. FastRP replaces that pipeline with iterative neighbor aggregation. A Rust/DataFusion implementation runs out-of-core, matched Node2Vec-level predictive quality in small tests, and processed an eight-million-node graph in about 15 minutes.