Detecting Research Fronts Using Neural Network Model for Weighted Citation Network Analysis

• Item type: JInfP(1)
• 公開日: 2015-11-15
• タイトル: Detecting Research Fronts Using Neural Network Model for Weighted Citation Network Analysis
• タイトル / 言語: en
• タイトル: Detecting Research Fronts Using Neural Network Model for Weighted Citation Network Analysis
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Special Issue on E-Service and Knowledge Management toward Smart Computing Society] citation network analysis, neural network, research fronts
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Department of Computer and Information Sciences, Faculty of Engineering, Tokyo University of Agriculture and Technology
• 著者所属: Department of Computer and Information Sciences, Faculty of Engineering, Tokyo University of Agriculture and Technology
• 著者所属(英): en
• 著者所属(英): Department of Computer and Information Sciences, Faculty of Engineering, Tokyo University of Agriculture and Technology
• 著者所属(英): en
• 著者所属(英): Department of Computer and Information Sciences, Faculty of Engineering, Tokyo University of Agriculture and Technology
• 著者名: Hisato, Fujimagari; Katsuhide, Fujita
• 著者名(英): Hisato, Fujimagari; Katsuhide, Fujita
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The performance of different types of weighted citation networks for detecting emerging research fronts was investigated by a comparative study in the existing work. The citation networks are constructed and then divided into clusters to detect the research front. Additionally, some measures to weighted citations like difference in publication years between citing and cited papers and similarities of keywords between them, which are expected to be able to effectively detect emerging research fronts, were applied. However, the functions of deciding the edge's weight in the citation networks are decided based on the experiments. For deciding the effective weight's functions automatically depending on the characteristics of the dataset, a learning method is important. In this paper, we propose the novel learning method based on the Neural Networks for deciding the edge's weights for the citation networks. We have been evaluating our proposed method in three research domains including Gallium nitride, Complex Networks, and Nano-carbon. We demonstrate that our proposed method has the best performance of each approach by using the following measures of extracted research fronts: visibility, speed, and topological and field relevance than the existing methods.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The performance of different types of weighted citation networks for detecting emerging research fronts was investigated by a comparative study in the existing work. The citation networks are constructed and then divided into clusters to detect the research front. Additionally, some measures to weighted citations like difference in publication years between citing and cited papers and similarities of keywords between them, which are expected to be able to effectively detect emerging research fronts, were applied. However, the functions of deciding the edge's weight in the citation networks are decided based on the experiments. For deciding the effective weight's functions automatically depending on the characteristics of the dataset, a learning method is important. In this paper, we propose the novel learning method based on the Neural Networks for deciding the edge's weights for the citation networks. We have been evaluating our proposed method in three research domains including Gallium nitride, Complex Networks, and Nano-carbon. We demonstrate that our proposed method has the best performance of each approach by using the following measures of extracted research fronts: visibility, speed, and topological and field relevance than the existing methods.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA00700121
• 書誌情報: Journal of information processing; 巻 23, 号 6, p. 753-758, 発行日 2015-11-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6652
• 出版者 / 言語: ja
• 出版者: 情報処理学会