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Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield

机译：期刊引用网络中的中间性和多样性作为跨学科性的衡量标准—对尤金·加菲尔德的致敬

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摘要

Journals were central to Eugene Garfield’s research interests. Among other things, journals are considered as units of analysis for bibliographic databases such as the Web of Science and Scopus. In addition to providing a basis for disciplinary classifications of journals, journal citation patterns span networks across boundaries to variable extents. Using betweenness centrality (BC) and diversity, we elaborate on the question of how to distinguish and rank journals in terms of interdisciplinarity. Interdisciplinarity, however, is difficult to operationalize in the absence of an operational definition of disciplines; the diversity of a unit of analysis is sample-dependent. BC can be considered as a measure of multi-disciplinarity. Diversity of co-citation in a citing document has been considered as an indicator of knowledge integration, but an author can also generate trans-disciplinary—that is, non-disciplined—variation by citing sources from other disciplines. Diversity in the bibliographic coupling among citing documents can analogously be considered as diffusion  or differentiation of knowledge across disciplines. Because the citation networks in the cited direction reflect both structure and variation, diversity in this direction is perhaps the best available measure of interdisciplinarity at the journal level. Furthermore, diversity is based on a summation and can therefore be decomposed; differences among (sub)sets can be tested for statistical significance. In the appendix, a general-purpose routine for measuring diversity in networks is provided.
机译：期刊对于Eugene Garfield的研究兴趣至关重要。除其他事项外，期刊被视为书目数据库（例如Web of Science和Scopus）的分析单位。除了为期刊的学科分类提供依据之外，期刊引文模式还跨越边界跨越网络，程度各异。利用中间性（BC）和多样性，我们阐述了如何根据跨学科性对期刊进行区分和排名的问题。然而，在缺乏学科的业务定义的情况下，跨学科很难实现；分析单元的多样性取决于样本。 BC可被视为衡量多学科性的指标。引用文档中同时被引用的多样性被认为是知识整合的一个指标，但是作者也可以通过引用其他学科的来源来产生跨学科的变化，即非学科的变化。引用文献之间书目耦合的多样性可以类似地视为跨学科知识的传播或分化。由于引用方向上的引用网络反映了结构和变化，因此该方向上的多样性也许是期刊一级跨学科性的最佳可用度量。此外，分集基于求和，因此可以分解；可以测试（子）集之间的差异的统计显着性。在附录中，提供了用于测量网络多样性的通用例程。

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