Betweenness and Diversity in Journal Citation Networks as Measures of Interdisciplinarity -- A Tribute to Eugene Garfield --

TL;DR

This paper explores how betweenness centrality and diversity measures can be used to assess and rank the interdisciplinarity of journals within citation networks, offering new operational tools for understanding knowledge integration.

Contribution

It introduces a novel approach to operationalize interdisciplinarity using betweenness centrality and diversity measures in journal citation networks.

Findings

Diversity in bibliographic coupling reflects knowledge diffusion across disciplines.

Betweenness centrality can serve as an indicator of multi-disciplinarity.

Diversity measures can be statistically tested for significance.

Abstract

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 (WoS) and Scopus. In addition to 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 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 an appendix, a general-purpose routine for measuring diversity in networks is provided.