• Journal of the Korean Society for information Management
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• v.32 no.4
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• pp.77-106
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• 2015

By document co-citation analysis with Korean Citation Index (KCI) data, this study accurately identified the research fronts and hot topics in Korean library and information science (LIS) from 2004 to 2013. 159 core papers in LIS domain and their citations are scraped manually from Korean Citation Index web site. In the cluster analysis and network analysis, 159 core papers were grouped into 27 clusters with multiple papers and 8 singlton clusters. Among the 27 clusters which have multple papers, 'LIS education' cluster was the largest with 16 core papers, and 'citation analysis & intellectual structure analysis' cluster had the strongest citation impact according to the ehs-index. Closer observation of the citations to the core papers in each research front showed that 67.5% of the citations were made by LIS research papers and 32.5% of the citations were made by non-LIS research papers. Considering the share of citations and the citation impact growth index, 'local documentation', 'citation analysis & intellectual structure analysis', and 'research trends analysis' were identified as the most emerging research front in Korean library and information science. The analytical methods used in this study have great potential in discovering the characteristics of research fronts in Korean interdisciplinary research domains.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.311-331
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• 2023

This study attempted to explore the characteristics of knowledge communication and investigate important research topics and key authors by analyzing major academic papers in the field of LIS in Korea for five years from 2018 to 2022. The research method collected and analyzed papers published for five years in four key journals in the field of domestic Library and Information Science from the Korean Citation Index (KCI) database. The paper was selected to extract the author data of the paper and the data of the reference, and network visualization was performed by conducting literature co-citation analysis and author co-citation analysis using Netminer. As a result of the analysis, it was possible to derive a pair of co-citations between authors, and it was confirmed that it is important to include multiple authors in the intellectual structure analysis in the academic field through co-citation frequency analysis among researchers. The literature confirmed the correlation between the topics of the paper, and it was found that research related to Library and Information Science was centered on the topics of library, digital, user, service, and data.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.204-209
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• 2023

Dynamic Bandwidth Allocation (DBA) methods in telecommunication network & systems have emerged with mechanisms for sharing limited resources in a rapidly growing number of users in today's access networks. Since the DBA research trends are incredibly fast-changing literature where almost every day new areas and terms continue to emerge. Co - citation analysis offers a significant support to researchers to distinguish intellectual bases and potentially leading edges of a specific field. We present the visualization based analysis for DBA algorithms in telecommunication field using mainstream co-citation analysis tool-CiteSpace and web of science (WoS) analysis. Research records for the period of decade (2009-2018) for this analysis are sought from WoS. The visualization results identify the most influential DBA algorithms research studies, journals, major countries, institutions, and researchers, and indicate the intellectual bases and focus entirely on DBA algorithms in the literature, offering guidance to interested researchers on more study of DBA algorithms.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.999-1011
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• 2013

In this research, we propose a document recommendation method which can find documents that are relatively important to a specific document based on citation information. The key idea is parameter tuning in the Neumann kernal which is an intermediate between a measure of importance (HITS) and of relatedness (co-citation). Our method properly selects the tuning parameter ${\gamma}$ in the Neumann kernal minimizing the prediction error in future citation. We also discuss some comutational issues needed for analysing large citation data. Finally, results of analyzing patents data from the US Patent Office are given.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.2
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• pp.119-143
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• 2011

This study identifies the characteristics of recent citation-based indicators for assessing a single paper in the context of their co-relationships. Five predefined indicators were examined with three variants of h-index which are convened in this study; the formers are PageRank, SCEAS Rank, CCI, f-value, and single paper h-index and the latters are $h_S$-index, h1-index, and $h_S$1-index. The correlation analysis and cluster analysis were performed to group the indicators by common characteristics, after which the indicators were calculated with the dataset from KSCI DB. The results show statistical evidence that distinguishes h-index type indicators from others. The characteristics of the indicators were verified with citation frequency factors using correlation analysis. Finally, the implications for applications and further studies are discussed.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.7-32
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• 2017

Recently, deep learning has been rapidly spreading as an innovative machine learning technique in various domains. This study explored the research trends of deep learning via modified ego centered topic citation analysis. To do that, a few seed documents were selected from among the retrieved documents with the keyword 'deep learning' from Web of Science, and the related documents were obtained through citation relations. Those papers citing seed documents were set as ego documents reflecting current research in the field of deep learning. Preliminary studies cited frequently in the ego documents were set as the citation identity documents that represents the specific themes in the field of deep learning. For ego documents which are the result of current research activities, some quantitative analysis methods including co-authorship network analysis were performed to identify major countries and research institutes. For the citation identity documents, co-citation analysis was conducted, and key literatures and key research themes were identified by investigating the citation image keywords, which are major keywords those citing the citation identity document clusters. Finally, we proposed and measured the citation growth index which reflects the growth trend of the citation influence on a specific topic, and showed the changes in the leading research themes in the field of deep learning.

• Journal of the Korean Society for information Management
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• v.27 no.1
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• pp.103-118
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• 2010

Recently, the valuation of patented technology has been greatly emphasized, and patent citation has been accepted as a very useful index of this technology. In this study, we performed correlation analyses between the patent citation counts and 17 explanatory variables of morphological, technological, and conceptual factors with a test dataset of U.S. patents in five subject fields. Seven variables having 5% or more standardized variances($r^2$) with patent citation counts were identified; number of pages, number of claims, reference-average-citation rate, patent increase/decrease rate, strength of bibliographic coupling, co-citation counts and document similarity. The result of the ANOVA test shows that the mean values of these variables vary among most subject fields.

• Journal of Information Science Theory and Practice
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• v.2 no.1
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• pp.35-47
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• 2014

Korean researchers' scientific outputs are increasing rapidly with the growth of both Korea's economy and industry size. This is the time to analyze the scientific output (STEM area) of Korea over the last 40 years. For this analysis, this study used the SCIE database of Web of Science (WoS), which is a representative global Citation Index Service. The objects of analysis are every Korean document published in the SCIE journal, and the research scopes are (1) analysis of citation and subject categories and (2) analysis of co-authorship. Through this study, characteristics such as cited trend and research trend by subject, and the status of collaboration between Korea and other countries including the 50 states of the USA were revealed.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.24-31
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• 2017

As the concept of sustainability becomes more and more popular, a large amount of literature have been recorded recently on intelligent green building and smart city (IGB&SC). It is therefore needed to systematically analyse the existing knowledge structure as well as the future new development of this domain through the identification of the thematic trends, landmark articles, typical keywords together with co-operative researchers. In this paper, Citespace software package is applied to analyse the citation networks and other relevant data of the past eleven years (from 2006 to 2016) collected from Web of Science (WOS). Through this, a series of professional document analysis are conducted, including the production of core authors, the influence made by the most cited authors, keywords extraction and timezone analysis, hot topics of research, highly cited papers and trends with regard to co-citation analysis, etc. As a result, the development track of the IGB&SC domains is revealed and visualized and the following results reached: (i) in the research area of IGB&SC, the most productive researcher is Winters JV and Caragliu A is most influential on the other hand; (ii) different focuses of IGB&SC research have been emerged continually from 2006 to 2016 e.g. smart growth, sustainability, smart city, big data, etc.; (iii) Hollands's work is identified with the most citations and the emerging trends, as revealed from the bursts analysis in document co-citations, can be concluded as smart growth, the assessment of intelligent green building and smart city.

• Journal of the Korean Society for information Management
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• v.33 no.2
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• pp.201-225
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• 2016

A new technique for revealing the research fronts of a interdisciplinary discipline has been developed. Citation image makers profiling (CIMP) determines the relationships between research papers with the title words of the citing documents. We adapted this new technique to analyze the research fronts and hot topics in women's studies of Korea. By Korean Citation Index (KCI) data in 2015, we selected 148 papers cited more than 9 times as the core documents of women's studies. Analysis of intellectual structure using citation image makers profiling was performed with the 148 core documents and those citing papers. Document co-citation analysis was hindered by citation data sparsity, while CIMP method successfully revealed the structure of research fronts of Korean women's studies including 2 divisions and 6 subdivisions. The CIMP method suggested in this study has good potential to discover the characteristics of research fronts of interdisciplinary research domains.