Abstract
Computer science has experienced dramatic growth and diversification over the last twenty years. Towards a current understanding of the structure of this discipline, we analyze a large sample of the computer science literature from the DBLP database. For insight on the features of this cohort and the relationship within its components, we have constructed article level clusters based on either direct citations or co-citations, and reconciled them with major and minor subject categories in the All Science Journal Classification. We describe complementary insights from clustering by direct citation and co-citation, and both point to the increase in computer science publications and their scope. Our analysis reveals cross-category clusters, some that interact with external fields, such as the biological sciences, while others remain inward looking. Overall, we document an increase in computer science publications and their scope.
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Acknowledgements
The authors thank Henry Small for very helpful discussions. Research and development reported in this publication was partially supported by funds from the National Institute on Drug Abuse, National Institutes of Health, US Department of Health and Human Services, under Contract No HHSN271201800040C (N44DA-18-1216). TW is supported by the Grainger Foundation. Citation data used in this paper relied on Scopus data as implemented in the ERNIE project (Korobskiy et al., 2019), which is collaborative between NET ESolutions Corporation and Elsevier Inc. We thank our Elsevier colleagues for their support of the ERNIE project.
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The authors declare that they have no conflicts of interest. Elsevier personnel played no role in conceptualization, experimental design, review of results, or conclusions presented. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, NET ESolutions Corporation, or Elsevier Inc.
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Devarakonda, S., Korobskiy, D., Warnow, T. et al. Viewing computer science through citation analysis: Salton and Bergmark Redux. Scientometrics 125, 271–287 (2020). https://doi.org/10.1007/s11192-020-03624-0
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DOI: https://doi.org/10.1007/s11192-020-03624-0