  The arXiv has collected 1.5 million pre-print articles over 28 years, hosting
literature from scientific fields including Physics, Mathematics, and Computer
Science. Each pre-print features text, figures, authors, citations, categories,
and other metadata. These rich, multi-modal features, combined with the natural
graph structure---created by citation, affiliation, and co-authorship---makes
the arXiv an exciting candidate for benchmarking next-generation models. Here
we take the first necessary steps toward this goal, by providing a pipeline
which standardizes and simplifies access to the arXiv's publicly available
data. We use this pipeline to extract and analyze a 6.7 million edge citation
graph, with an 11 billion word corpus of full-text research articles. We
present some baseline classification results, and motivate application of more
exciting generative graph models.
