Visual topical analysis of Chinese and American Library and Information Science research institutions |
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| Institution: | 1. School of Information Management, Wuhan University, Luojia Shan, Wuhan, Hubei Province 430072, PR China;2. School of Information and Safety Engineering, Zhongnan University of Economics and Law, 182# Nanhu Avenue, East Lake High-tech Development Zone, Wuhan 430073, PR China;1. The Graduate School of Public Policy and Information Technology, Seoul National University of Science and Technology, 172 Gongreung 2-dong, Nowon-gu, Seoul 139-746, Republic of Korea;2. Department of Systems Management Engineering, Sungkyunkwan University, 300 Chunchun-dong, Jangan-gu, Kyunggi-do 440-746, Republic of Korea;1. Laboratory for Studies of Research and Technology Transfer, Institute for System Analysis and Computer Science (IASI-CNR), National Research Council of Italy, Italy;2. Italian National Agency for the Evaluation of Universities and Research Institutes (ANVUR), Italy;3. Department of Management and Engineering University of Rome “Tor Vergata”, Italy;1. Department of Library and Information Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan;2. School of Information, University of Michigan, Ann Arbor, MI, USA;3. Department of Mechanical Engineering and Institute of Industrial Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan;1. CNR-CERIS, National Research Council of Italy, Institute for Economic Research on Firm and Growth, via Real Collegio, 30, I-10024 Moncalieri (TO), Italy;2. University of Torino, Department of Chemistry, via P. Giuria, 7, I-10125 Torino, Italy |
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| Abstract: | Research institutions play an important role in scientific research and technical innovation. The topical analysis of research institutions in different countries can facilitate mutual learning and promote potential collaboration. In this study, we illustrate how an unsupervised artificial neural network technique Self-Organizing Map (SOM) can be used to visually analyze the research fields of research institutions. A novel SOM display named Compound Component Plane (CCP) was presented and applied to determine the institutions which made significant contributions to the salient research fields. Eighty-seven Chinese and American LIS institutions and the technical LIS fields were taken as examples. Potential international and domestic collaborators were identified based upon their research similarities. An approach of dividing research institutions into clusters was proposed based on their geometric distances in the SOM display, the U-matrix values and the most salient research topics they involved. The concepts of swarm institutions, pivots and landmarks were also defined and their instances were identified. |
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| Keywords: | Self-Organizing Map Compound Component Plane Topical analysis Research institution |
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