Network Analysis for the Digital Humanities: Principles, Problems, Extensions
Network Analysis for the Digital Humanities: Principles, Problems, Extensions
复制标题
数字人文网络分析：原理、问题、扩展
作者：
Painter, Deryc T.;Daniels, Bryan C.;Jost, Jürgen
传统的史学很难跟上现代科学出版趋势的快速步伐。即使专注于一个特定的科学领域，新发表论文的速度也远远超过了最勤奋的历史学家的研究能力。本文总结了一种利用网络分析的计算技术来解决这个问题的方法。作为对特定文档近距离分析的补充，网络分析可以提供关于科学史的大规模视角，识别大量文档的关系模式，否则可能需要整个职业生涯才能消化这些模式。为了证明这种方法的力量，本文将网络理论应用于进化医学的出版物语料库。四个不同的网络，包括那些专注于作者、关键字和引文的网络，迅速挖掘出一系列相关的历史信息。这篇文章说明了如何从各种量化指标中得出可解释的历史结论。其目的是为历史学家提供网络技术的概述，以期为他们的研究剧目增加强大的网络分析。
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.
语料库设计标准
DOI：
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发表时间：
1992
期刊：
影响因子：
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作者：
Sue Atkins;Jeremy Clear;N. Ostler
通讯作者：
N. Ostler