Search (2 results, page 1 of 1)

  • × author_ss:"Matsushima, K."
  • × theme_ss:"Informetrie"
  • × year_i:[2000 TO 2010}
  1. Shibata, N.; Kajikawa, Y.; Takeda, Y.; Matsushima, K.: Comparative study on methods of detecting research fronts using different types of citation (2009) 0.02 
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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.
    Date
    22. 3.2009 17:52:50
    Type
    a
  2. Shibata, N.; Kajikawa, Y.; Matsushima, K.: Topological analysis of citation networks to discover the future core articles (2007) 0.00 
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    Abstract
    In this article, we investigated the factors determining the capability of academic articles to be cited in the future using a topological analysis of citation networks. The basic idea is that articles that will have many citations were in a "similar" position topologically in the past. To validate this hypothesis, we investigated the correlation between future times cited and three measures of centrality: clustering centrality, closeness centrality, and betweenness centrality. We also analyzed the effect of aging as well as of self-correlation of times cited. Case studies were performed in the two following recent representative innovations: Gallium Nitride and Complex Networks. The results suggest that times cited is the main factor in explaining the near future times cited, and betweenness centrality is correlated with the distant future times cited. The effect of topological position on the capability to be cited is influenced by the migrating phenomenon in which the activated center of research shifts from an existing domain to a new emerging domain.
    Type
    a

Authors