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.

本文介绍了 arXiv，它收集了来自物理学、数学和计算机科学等科学领域的 150 万篇预先印刷的文章，并提供了一个管道，标准化和简化 arXiv 的公开可用数据。该管道被用来提取和分析一个拥有 670 万边的引文图和一个包含 110 亿字的全文研究文章语料库。作者呈现了一些基线分类结果并推动了更加令人兴奋的生成图模型的应用。