Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial applications, contain billions of nodes and trillions of edges, which exceeds the capability of existing embedding systems. We present PyTorch-BigGraph (PBG), an embedding system that incorporates several modifications to traditional multi-relation embedding systems that allow it to scale to graphs with billions of nodes and trillions of edges. PBG uses graph partitioning to train arbitrarily large embeddings on either a single machine or in a distributed environment. We demonstrate comparable performance with existing embedding systems on common benchmarks, while allowing for scaling to arbitrarily large graphs and parallelization on multiple machines. We train and evaluate embeddings on several large social network graphs as well as the full Freebase dataset, which contains over 100 million nodes and 2 billion edges.

通过对现有的图嵌入系统进行多方面调整，将图划分为更小的子图进行处理，我们提出了创新型的PyTorch-BigGraph (PBG)嵌入系统，其可以扩展到包含数十亿个节点和万亿条边的图谱，使得我们能够对大型社交网络图和Freebase数据集等各种大型图谱进行分布式处理和大规模分析。

PyTorch-BigGraph：大规模图嵌入系统