본문 바로가기
  • Home

Deep Learning Research Trends Analysis with Ego Centered Topic Citation Analysis

  • Journal of the Korean Society for Information Management
  • Abbr : JKOSIM
  • 2017, 34(4), pp.7~32
  • DOI : 10.3743/KOSIM.2017.34.4.007
  • Publisher : 한국정보관리학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : September 28, 2017
  • Accepted : October 11, 2017
  • Published : December 30, 2017

Lee, Jae Yun 1

1명지대학교

Accredited

ABSTRACT

Recently, deep learning has been rapidly spreading as an innovative machine learning technique in various domains. This study explored the research trends of deep learning via modified ego centered topic citation analysis. To do that, a few seed documents were selected from among the retrieved documents with the keyword ‘deep learning’ from Web of Science, and the related documents were obtained through citation relations. Those papers citing seed documents were set as ego documents reflecting current research in the field of deep learning. Preliminary studies cited frequently in the ego documents were set as the citation identity documents that represents the specific themes in the field of deep learning. For ego documents which are the result of current research activities, some quantitative analysis methods including co-authorship network analysis were performed to identify major countries and research institutes. For the citation identity documents, co-citation analysis was conducted, and key literatures and key research themes were identified by investigating the citation image keywords, which are major keywords those citing the citation identity document clusters. Finally, we proposed and measured the citation growth index which reflects the growth trend of the citation influence on a specific topic, and showed the changes in the leading research themes in the field of deep learning.

Citation status

* References for papers published after 2023 are currently being built.