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Graph embedding methods produce unsupervised node features from graphs thatcan then be used for a variety of machine learning tasks. Modern graphs,particularly in industrial applications, contain billions of nodes andtrillions of edges, which exceeds the capability of existing embedding systems.We present PyTorch-BigGraph (PBG), an embedding system that incorporatesseveral modifications to traditional multi-relation embedding systems thatallow it to scale to graphs with billions of nodes and trillions of edges. PBGuses graph partitioning to train arbitrarily large embeddings on either asingle machine or in a distributed environment. We demonstrate comparableperformance with existing embedding systems on common benchmarks, whileallowing for scaling to arbitrarily large graphs and parallelization onmultiple machines. We train and evaluate embeddings on several large socialnetwork graphs as well as the full Freebase dataset, which contains over 100million nodes and 2 billion edges.

PyTorch-BigGraph: A Large-scale Graph Embedding System