2026-03-08T04:38:28Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00145547 2025-01-20T06:42:47Z 581:7706:7716

Detecting Research Fronts Using Neural Network Model for Weighted Citation Network Analysis Detecting Research Fronts Using Neural Network Model for Weighted Citation Network Analysis Hisato, Fujimagari Katsuhide, Fujita Hisato, Fujimagari Katsuhide, Fujita [特集：E-Service and Knowledge Management toward Smart Computing Society] citation network analysis, neural network, research fronts 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. \n------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.23(2015) No.6 (online) ------------------------------ 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. \n------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.23(2015) No.6 (online) ------------------------------ journal article 2015-10-15 application/pdf 情報処理学会論文誌 10 56 1882-7764 AN00116647 https://ipsj.ixsq.nii.ac.jp/record/145547/files/IPSJ-JNL5610004.pdf eng