Traditional historical scholarship struggles to keep up with the rapid pace of modern scientific publication trends. Even focusing on a particular scientific field, the rate of new publications far outpaces even the most studious historian’s research capacity. This essay summarizes an approach to this problem that uses computational techniques of network analysis. As a complement to close analysis of particular documents, network analysis can give a large-scale perspective on the history of science, identifying relational patterns across a vast number of documents that might otherwise require an entire career to digest. To demonstrate the power of the approach, the essay applies network theory to a corpus of publications in evolutionary medicine. Four distinct networks, including those focused on authors, keywords, and citations, quickly unearth a range of relevant historical information. The essay illustrates how interpretable historical conclusions are drawn from a variety of quantitative metrics. The aim is to provide an overview of network techniques for historians looking to add robust network analysis to their research repertoire.

中文翻译：

---

数字人文网络分析：原理、问题、扩展

传统的历史学术努力跟上现代科学出版趋势的快速步伐。即使专注于特定的科学领域，新出版物的速度也远远超过最勤奋的历史学家的研究能力。本文总结了使用网络分析计算技术解决此问题的方法。作为对特定文档的密切分析的补充，网络分析可以提供科学史的大规模视角，识别大量文档中的关系模式，否则可能需要整个职业来消化。为了证明该方法的威力，本文将网络理论应用于进化医学领域的出版物语料库。四个不同的网络，包括那些专注于作者、关键词和引文的网络，快速发掘一系列相关历史信息。这篇文章说明了如何从各种定量指标中得出可解释的历史结论。其目的是为希望将稳健的网络分析添加到他们的研究范围内的历史学家提供网络技术的概述。