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Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer.

David Opeoluwa Oyewola1, Emmanuel Gbenga Dada2

  • 1Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B 0182, Gombe, Nigeria.

SN Applied Sciences
|April 18, 2022
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Summary

This study analyzed 10,814 machine learning publications (2010-2020) to map global research collaboration. Key institutions like Harvard and NUS exhibit high centrality, indicating significant influence in the machine learning research landscape.

Keywords:
BibliometrixCouplingMachine learningScientometricsVOSviewer

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Machine Learning (ML) is pivotal across diverse sectors including healthcare, transportation, and security.
  • Understanding the global research structure of ML is crucial for identifying collaboration patterns and influential institutions.

Purpose of the Study:

  • To analyze bibliographic coupling, institutional collaboration, and country co-authorship networks in ML publications.
  • To identify dominant research institutions and their influence using network analysis.

Main Methods:

  • Extracted 10,814 ML publications (2010-2020) from the Dimensions database.
  • Applied Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for author dominance ranking.
  • Utilized Bibliometrix for data analysis and VOSviewer for network visualization.

Main Results:

  • Identified high betweenness centrality for institutions like University of California, Harvard University, National University of Singapore, University of Cambridge, and Imperial College London.
  • These central institutions demonstrate significant control over collaborative relationships and research resources.
  • Revealed distinct clusters of research activity and collaboration.

Conclusions:

  • The study provides a comprehensive structural analysis of the global machine learning research landscape.
  • Findings offer valuable insights for future research directions and strategic collaborations in ML.
  • Highlights the influential role of specific universities in shaping ML research trends.