发表机构
Huawei Research; Politecnico di Torino（华为研究院; 都灵理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GitScholar数据集将GitHub参与度与AI论文关联，证明其能提升影响力预测精度达12%，并覆盖高影响力论文。
AI 中文摘要
随着AI研究的快速发展以及每天数百篇新论文的发表，跟上最新进展变得越来越困难。对于研究人员而言，快速识别有影响力的工作至关重要，但手动审阅每一篇新论文并不现实。自动化影响力预测方法通常通过结合各种可用信息源（如论文内容或引用历史）来帮助应对这一挑战。在本工作中，我们提出将GitHub参与度作为额外信息源，并证明它能提供既及时又准确的信号。为此，我们引入了GitScholar，这是一个新颖的数据集，将来自444,000个代码仓库的GitHub活动与超过558,000篇AI领域的arXiv论文关联起来。我们的实验表明，与强学术基线相比，GitHub反应（reactions）将早期预测精度提高了多达12%。此外，我们发现GitHub信号对高影响力AI论文提供了近乎完整的覆盖，并始终与未来的学术成功相关。GitScholar可在以下网址公开获取：此https URL。
英文摘要
With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingly difficult. For researchers, quickly identifying impactful work is essential, yet manually reviewing each new publication is impractical. Automated impact prediction methods help address this challenge, usually by combining various information sources available, such as a paper's content or citation history. In this work, we propose using GitHub engagement as an additional source and demonstrate that it provides both a timely and accurate signal. To this end, we introduce GitScholar, a novel dataset that links GitHub activity from 444,000 repositories to over 558,000 AI arXiv papers. Our experiments show that GitHub reactions improve early prediction precision by up to 12% over a strong academic baseline. Additionally, we find that GitHub signal offers near-complete coverage of high-impact AI papers, and consistently correlates with future academic success. GitScholar is publicly available at https://huggingface.co/datasets/huawei-csl/GitScholar.
