AI 中文总结
研究分析论文与其来源间的引用网络，借助大语言模型提取来源，用两种提示发现生成的骨干网络有相似特征，入度分布异质性高，多数高被引论文相同，作者引用排名稳定，完整引用网络能可靠反映论文和作者相对引用影响力。
AI 中文摘要
科学论文的参考文献与论文内容相关性各异。若能识别实际启发论文的来源作品，其引用比标准引用计数更能揭示科学项目起源及论文和作者的实际重要性。本文分析了科学的支柱，即论文与其来源间的引用网络。通过大语言模型从论文全文中提取来源，用两种相关提示发现大语言模型选出的参考文献少且非随机，生成的骨干网络在入度分布、模块化、传递性和度相关性方面相似。骨干网络入度分布异质性更高，多数高被引论文相同，作者间引用排名也稳定。结论是完整引用网络虽冗余，但能可靠反映论文和作者的相对引用影响力。
英文摘要
The bibliography of scientific papers lists items with variable degree of relevance for the contents of the paper itself. If we could identify the sources, i.e., the works that actually inspired the paper, their citations can help us uncover the genesis of scientific projects and would be more representative of the actual importance of papers and authors than the standard citation counts, when all references are considered. Here we present an analysis of the \textit{backbone of science}, i.e., the network of citations between papers and their sources. The latter are extracted from the full body of papers via Large Language Models (LLMs), which are currently very capable of correctly identifying the context in which a paper is cited. Using two different but related prompts, we find that the LLMs select only a small set of references, not taken at random, and that the resulting backbone networks are quite similar to each other with respect to their in-degree distributions, modularity, transitivity, and degree correlations. Backbone networks have higher heterogeneity in their in-degree distributions, compared to the full network, but the most cited papers are usually the same, with some important exceptions. Citation rankings among authors are also remarkably stable. We conclude that the full citation network, despite its redundancy with respect to the backbones, presents a reliable picture of the relative citation impact of papers and authors.
Comments26 pages, 8 figures, and 21 tables
