• Journal of Fisheries and Marine Sciences Education
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• v.27 no.1
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• pp.108-114
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• 2015

The purpose of this study is to find the relationships among communication competency, social network centrality(trust centrality and knowledge sharing centrality), discussion performance, and online boarding activity in the team based learning situation. For investigating this topic, 44 students are participated in the classes of educational technology. In order to find out the relationships among communication competency, social network centrality, discussion performance, and online boarding activity, compared t-test and path analysis are used. Followings are the results of the research: (a) Communication competency is improved significantly after team based learning. (b) Trust centrality effects significantly on the knowledge sharing centrality. (c) Knowledge sharing effects significantly on discussion performance. (d) Trust centrality effects on the online boarding activity in the team based learning.

• Korean Institute of Interior Design Journal
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• v.15 no.2 s.55
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• pp.56-64
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• 2006

From ancient times to today the formation of centrality in architecture has taken very important role as an organizing device. However after 19th century modem architecture focused on the universal aspect and homogeneity of space, and developed its own vocabulary through de-centralization. Since 1960's the reflections on de-humanized space of modem space have been evloved. As those movement has been understood seriously, the centrality can be changed to phenomenological one. And The phenomenological centrality maintains organic balance with its surroundings not trying to form a hierarchic system. So it shows human-oriented tendency and facilitates experiential movements of human body. And it also gives a sense of being. The phenomenological centrality is comprised of center, boundary, and two types of axis. The characteristics of phenomenological centrality can be analyzed as practical and meaningful center, visible boundary, experiential movement, and unexpectable encounters with spaces and nature.

• Health Policy and Management
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• v.29 no.2
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• pp.138-145
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• 2019

Background: The purpose of this study is supposed to figure out the keyword network from 2009 to 2018 with social network analysis and provide the research data that can help the Korea government's policy making on smoking cessation. Methods: First, frequency analysis on the keyword was performed. After, in this study, I applied three classic centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) with R 3.5.1. Moreover, I visualized the results as the word cloud and keyword network. Results: As a result of network analysis, 'smoking' and 'smoking cessation' were key words with high frequency, high degree centrality, and betweenness centrality. As a result of looking at trends in keyword, many study had been done on the keyword 'secondhand smoke' and 'adolescent' from 2009 to 2013, and 'cigarette graphic warning' and 'electronic cigarette' from 2014 to 2018. Conclusion: This study contributes to understand trends on smoking cessation study and seek further study with the keyword network analysis.

• Journal of the Korean Society for information Management
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• v.32 no.2
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• pp.7-23
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• 2015

While there are several measures for node centralities, such as betweenness and degree, few centrality measures for local centralities in weighted networks have been suggested. This study developed a generalized centrality measure for calculating local centralities in weighted networks. Neighbor centrality, which was suggested in this study, is the generalization of the degree centrality for binary networks and the nearest neighbor centrality for weighted networks with the parameter ${\alpha}$. The characteristics of suggested measure and the proper value of parameter ${\alpha}$ are investigated with 6 real network datasets and the results are reported.

• Journal of Agricultural Extension & Community Development
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• v.27 no.3
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• pp.125-134
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• 2020

The regional centrality plays a very important role in national and regional planning and it is measured by data such as people, goods, and information flows among regions. The inter-regional flows are usually considered by only direct flows, yet indirect flows, which are generated accordingly from direct flows, are not critically considered. Most centrality studies have also hardly reflected the indirect flow in the network analysis. This study demonstrates the significance of the indirect flows to enhance accuracy of the regional centrality. The nationwide dataset of inter-regional commuter traffic matrix is used in this study and analysed into two groups; one to consider only direct flow and the other both direct and indirect flows. The results indicate remarkable differences of centrality raking between two groups such as Yeongam of Jeonnam Province(+60th), Eumseong of Chungbuk Province(+57th), Gwacheon of Gyeonggi Province (-35th), and Nowon of Seoul (-32nd). It clearly shows the significant influence of indirect flow for regional centrality study. This also reveals regional centrality ranking in Korea by considering direct and indirect flows of commuters. Jung, Gangnam, and Jongno of Seoul are categorized in the highest rank group and Ulleung of Gyeongbuk, Ongjin of Incheon, and Jindo of Jeonnam are in the lowest group. The top group includes seven districts of Seoul, two of Busan, and one of Gyeonggi Province. The bottom group includes mostly island and costal areas. As this study shows an accurate method of centrality measurement, it has a significant implication to lead an effective regional planning.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.47-56
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• 2020

The purpose of this study was to identify the research trends and characteristics of existing research related to the AEO system. The methodology of the study was to utilize the Degree Centrality, Closeness Centrality and Betweenness Centrality presented by the Social Network Analysis (SNA). Keyword network analysis results showed that "MRA", "Logistics Security" were derived from the Degree Centrality results, "MRA", "Logistics Security" from the Closeness Centrality results, and, as a result of the Betweenness Centrality, "AEO Utilization Benefits" and "reliability" were derived from the top keyword results. The analysis of differences in centrality by period also confirmed that trends in research have changed based on specific time points. This study has implications for the study in that it presented worldwide research trends through keyword network analysis of the AEO system.

• Journal of the Korean Home Economics Association
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• v.40 no.10
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• pp.87-102
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• 2002

The purpose of this study is to investigate the social centrality and antisocial behavior according to adolescents' popularity. The 809 subjects are selected from the first and second graders of middle and high schools. The main findings of this study are as follows: 1) The social centrality of popular adolescents is higher than that of average adolescents and rejected adolescents. 2) For high school students, female students have higher social centrality than male students. 3) For female students, high school students have higher social centrality than middle school students. 4) The antisocial behavior score of popular adolescents is not different in that of average adolescents and rejected adolescents. That is, popular adolescents also show antisocial behavior. 5) The antisocial behavior score of male students is higher than that of female students. 6) The antisocial behavior score of high school students is higher than that of middle school students.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.149-163
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• 2023

In this study we analyze research trends on machine learning healthcare based on papers from the United States, the United Kingdom, and Korea. In Elsevier's Scopus, we collected 3425 papers related to machine learning healthcare published from 2018 to 2022. Keyword frequency and centrality analysis were conducted using the abstracts of the collected papers. We identified keywords with high frequency of appearance by calculating keyword frequency and found central research keywords through the centrality analysis by country. Through the analysis results, research related to machine learning, deep learning, healthcare, and the covid virus was conducted as the most central and highly mediating research in each country. As the implication, studies related to electronic health information-based treatment, natural language processing, and privacy in Korea have lower degree centrality and betweenness centrality than those of the United States and the United Kingdom. Thus, various convergence research applied with machine learning is needed for these fields.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.1
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• pp.1-15
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• 2024

This study aims to analyze research trends in blockchain-based Internet of Things focusing on the US, UK, and Korea. In Elsevier's Scopus, we collected 2,174 papers about blockchain-based Internet of Things published in from 2018 to 2023. Keyword frequency and centrality analysis were conducted on the abstracts of the collected papers. Through the obtained keyword frequencies, we tried to identify keywords with high frequency of occurrence and through centrality analysis, we tried to identify central research keywords for each country. As a result of the centrality analysis, research on blockchain, smart contracts, Internet of Things, security and personal information protection was conducted as the most central research in each country. The implication for Korea is that cybersecurity, authentication research appears to have been conducted with a lower centrality compared to the United States and the United Kingdom. Thus, it seems that intensive research related to cybersecurity and authentication is needed.

• Journal of the Korean Society of Costume
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• v.65 no.6
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• pp.25-35
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• 2015

The purpose of this study is to investigate the characteristics of members and communities that have significant influence in the online fashion community through their word-of-mouth activities. In order to identify the influence and the diffusion of word-of-mouth in fashion community, the study selected one online fashion community. Then, the study sorted the online posts and comments made on fashion information and put them into the matrix form to perform social network analysis. The result of the analysis is as follows: First, the fashion community network used in the study has many active members that relay information very quickly. Average time for information diffusion is very short, taking only one or two days in most cases. Second, the influence of word-of-mouth is led by key information produced from only a few members. The number of influential members account for less than 20% of the total number of community members, which indicate high level of degree centrality. The diffusion of word-of-mouth is led by even fewer members, which represent high level of betweenness centrality, compared to the case of degree centrality. Third, component characteristic shares similar information with about 70% of all members being linked to maximize information influence and diffusion. Fourth, a node with high degree centrality and betweenness centrality shares similar interests, presenting strain effect to particular information. Specially, members with high betweenness centrality show similar interests with members of high degree centrality. The members with high betweenness centrality also help expansion of related information by actively commenting on posts. The result of this research emphasizes the necessity of creation and management of network to efficiently convey fashion information by identifying key members with high level of information influence and diffusion to enhance the outcome of online word-of-mouth.