Comparative study on methods of detecting research fronts using different types of citation

  • Authors:

  • Naoki Shibata;Yuya Kajikawa;Yoshiyuki Takeda;Katsumori Matsushima

  • Affiliations:

  • Institute of Engineering Innovation, School of Engineering, University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-8656, Japan;Institute of Engineering Innovation, School of Engineering, University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-8656, Japan;Institute of Engineering Innovation, School of Engineering, University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-8656, Japan;Institute of Engineering Innovation, School of Engineering, University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-8656, Japan

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2009

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Abstract

In this article, we performed a comparative study to investigate the performance of methods for detecting emerging research fronts. Three types of citation network, co-citation, bibliographic coupling, and direct citation, were tested in three research domains, gallium nitride (GaN), complex network (CNW), and carbon nanotube (CNT). Three types of citation network were constructed for each research domain, and the papers in those domains were divided into clusters to detect the research front. We evaluated the performance of each type of citation network in detecting a research front by using the following measures of papers in the cluster: visibility, measured by normalized cluster size, speed, measured by average publication year, and topological relevance, measured by density. Direct citation, which could detect large and young emerging clusters earlier, shows the best performance in detecting a research front, and co-citation shows the worst. Additionally, in direct citation networks, the clustering coefficient was the largest, which suggests that the content similarity of papers connected by direct citations is the greatest and that direct citation networks have the least risk of missing emerging research domains because core papers are included in the largest component. © 2009 Wiley Periodicals, Inc.