VOLUME 13 NUMBER 2 (July to December 2020)

PSL%202019 vol12-no02-p133-138-Mikita%20and%20Padlan

Philipp. Sci. Lett. 2020 13 (2) 148-157
available online: October 31, 2020

*Corresponding author
Email Address: angelyn.lao@dlsu.edu.ph
Date received: April 1, 2020
Date revised: August 14, 2020
Date accepted: August 25, 2020


Analysis of the co-authorship network of Filipino researchers in deep learning

Angelyn R. Lao*1,2, Unisse C. Chua2,3, and Briane Paul V. Samson2,3,4

1Mathematics and Statistics Department,
      De La Salle University, 0922 Manila, Philippines
2Center for Complexity and Emerging Technologies,
      De La Salle University, 0922 Manila, Philippines
3Software Technology Department,
      De La Salle University, 0922 Manila, Philippines
4Future University Hakodate, Hakodate, Japan
Deep learning is an emerging field that attracts attention from various researchers to use in analyzing and understanding increasingly complex systems and phenomena. Crucial to the advancement of deep learning research in the Philippines, institutions need to understand its direction and reach out and work with other leading researchers who have knowledge, insight, and resources to contribute to the challenges in deep learning research. Due to its wide application, we analyzed the collaboration dynamics and patterns of Filipino researchers working in this field through their co-authorship network. Our study found that while there is a steady increase in research productivity on deep learning, most of the publications and collaborations are concentrated on a handful of institutions like De La Salle University and University of the Philippines Diliman. Despite their current control over the direction of the local research community, these two top institutions have yet to form strong collaborations. We also found that Filipino researchers are mostly doing applied deep learning projects and rely heavily on undergraduate students to maintain productivity.

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