Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes

Jin Mao, Zhentao Liang, Yujie Cao and Gang Li

Journal of Informetrics, 2020, vol. 14, issue 4

Abstract: Knowledge flow between disciplines is typically measured through citations among publications. In this study, we quantify cross-disciplinary knowledge diffusion from the novel perspective of content by introducing knowledge memes, a special type of knowledge unit. Diffusion cascade is proposed to model the diffusion process of knowledge memes. By taking Medical Informatics (MI) as an exemplary interdisciplinary discipline, we measure the knowledge relationships between it and four related disciplines. The diffusion patterns of cross-disciplinary memes are also identified by analyzing the network structure of the diffusion cascade. The results present the knowledge relationships among disciplines measured by knowledge memes, which are different from those measured by citations. It is shown that preferential attachment takes effect in cross-disciplinary knowledge meme diffusion. In addition, cross-disciplinary knowledge memes generally originate earlier and have higher impact than the memes of MI. This study provides insights into new approaches to quantifying knowledge relationships among disciplines and furthers the understanding of content diffusion mechanisms through measurable knowledge units.

Keywords: Knowledge diffusion; Cascade; Interdisciplinary research; Diffusion pattern; Knowledge relationship (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:14:y:2020:i:4:s1751157720301516

DOI: 10.1016/j.joi.2020.101092

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