GitScholar 数据集：用 GitHub 活跃度预测 AI 论文影响力
GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement
华为团队把 44 万个 GitHub 仓库和 55 万篇 arXiv 论文连起来，发现星标等信号能提前判断哪篇论文会火，精度提升 12%，数据集免费下载。
华为中央研究院团队发布 GitScholar 数据集，将 444,000 个 GitHub 仓库的活跃数据与 558,000 篇 arXiv AI 论文关联。
实验显示，加入 GitHub 反应信号后，早期影响力预测的精度相比强学术基线提升最高 12%。数据集覆盖高影响力 AI 论文接近完整，且 GitHub 信号与后续学术成功持续相关。
GitScholar 已在 Hugging Face 上公开。
GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement
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.
本条情报归属 AITOP「论文」核心栏目，重点聚焦中国 AI 产业与国产大模型生态。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：GitScholar、华为、GitHub、论文影响力预测，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Emilien Guandalino, Lorenz K. Müller, Beatrice Alessandra Motetti, Konstantin Berestizshevsky, Lukas Cavigelli），经 AITOP 多维评分管线结构化提炼。
