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[1903.12287] PyTorch-BigGraph: A Large-scale Graph Embedding System
Source: arxiv.org
Article summary:
1. PyTorch-BigGraph (PBG) is a graph embedding system that can scale to graphs with billions of nodes and trillions of edges.
2. PBG uses graph partitioning to train arbitrarily large embeddings on either a single machine or in a distributed environment.
3. PBG has been tested on several large social network graphs as well as the full Freebase dataset, which contains over 100 million nodes and 2 billion edges.
Article analysis:
The article is generally trustworthy and reliable, as it provides evidence for its claims and presents both sides of the argument equally. The authors have provided evidence for their claims by demonstrating comparable performance with existing embedding systems on common benchmarks, while allowing for scaling to arbitrarily large graphs and parallelization on multiple machines. Furthermore, they have tested PBG on several large social network graphs as well as the full Freebase dataset, which provides further evidence for their claims. There are no obvious biases or unsupported claims in the article, nor any missing points of consideration or missing evidence for the claims made. All counterarguments are explored and presented equally, there is no promotional content, partiality or one-sided reporting present in the article. The article does note possible risks associated with using PBG such as scalability issues when dealing with very large datasets. In conclusion, this article is trustworthy and reliable due to its balanced presentation of both sides of the argument and its use of evidence to support its claims.