Uncovering diffusion trends in computer science and physics publications

Abstract

Purpose The purpose of this paper is to trace the knowledge diffusion patterns between the publications of top journals of computer science and physics to uncover the knowledge diffusion trends. Design/methodology/approach The degree of information flow between the disciplines is a measure of entropy and received citations. The entropy gives the uncertainty in the citation distribution of a journal; the more a journal is involved in spreading information or affected by other journals, its entropy increases. The citations from outside category give the degree of inter-disciplinarity index as the percentage of references made to papers of another discipline. In this study, the topic-related diffusion across computer science and physics scholarly communication network is studied to examine how the same research topic is studied and shared across disciplines. Findings For three indicators, Shannon entropy, citations outside category (COC) and research keywords, a global view of information flow at the journal level between both disciplines is obtained. It is observed that computer science mostly cites knowledge published in physics journals as compared to physics journals that cite knowledge within the field. Originality/value To the best of the authors’ knowledge, this is the first study that traces knowledge diffusion trends between computer science and physics publications at journal level using entropy, COC and research keywords.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 12 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.