• Title, Summary, Keyword: Degree Centrality

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Effects of Centrality on IT Usage Capability : A Perspective of Social Networks (조직 내 중심성이 IT활용능력에 미치는 영향: 소셜네트워크 관점)

  • Kim, Hyo-Jun;Kwahk, Kee-Young
    • The Journal of Information Systems
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    • v.20 no.1
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    • pp.147-169
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    • 2011
  • In organizations, evaluating the competency of individuals through the position or status has many limitations. To overcome these limitations, this study analyzes the organization's informal network using social network analysis. We measured out-degree centrality and in-degree centrality by making use of social network analysis technique. Out-degree centrality is interpreted as 'madangbal' in that actors actively help other people, while in-degree centrality is interpreted as 'prestige' in that other people want to have a relationship with. This research examines the effects of individual's 'prestige' and 'madangbal' in the instrumental network and communication network on IT competency. We carried out empirical analysis using social network data that were collected from undergraduate students. The result reveals that relationship between IT competency and centrality in the instrumental network is statistically significant, while relationship between IT competency and centrality in the communication network does not show significant results.

Analyzing the Spatial Centrality of Rural Villages for Green-Tourism using GIS and Social Network Analysis -Focusing on Rural Amenity and Human Resources- (GIS 및 사회네트워크 분석을 통한 농촌마을 관광중심성 분석 -농촌어메니티 자원 및 인적자원을 중심으로-)

  • Lee, Sang-Hyun;Choi, Jin-Yong;Bae, Seung-Jong;Oh, Yun-Gyeong
    • Journal of Korean Society of Rural Planning
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    • v.15 no.1
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    • pp.47-59
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    • 2009
  • The aim of this study is to analyze the green-tourism centrality considering spatial interaction using Gravity Model and social network method. The degree centrality and prestige centrality were applied as green-tourism centrality index. The rural amenity resources and human resources were counted as attraction factors, and a distance among villages was used as friction factor in gravity model. The weights of rural tourism amenity resources were calculated using the analytic hierarchy process(AHP) method and applied to evaluate green-tourism potentiality. The distance was measured with the shortest path among villages using geographic information system(GIS) network analysis. The spatial interaction from gravity model were employed as link weights between nodal points; a pair villages. Using the spatial interaction, the degree-centrality and prestige-centrality indices were calculated by social network analysis and demonstrated possibility of developing integrated green-tourism region centered on high centrality villages.

Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks (문헌 단위 인용 네트워크 내 인용과 중심성 지수 간 관계 추정에 관한 연구)

  • Yu, So-Young
    • Journal of the Korean Society for information Management
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    • v.29 no.2
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    • pp.193-204
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    • 2012
  • The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

Study on Influence and Diffusion of Word-of-Mouth in Online Fashion Community Network (온라인 패션커뮤니티 네트워크에서의 구전 영향력과 확산력에 관한 연구)

  • Song, Kieun;Lee, Duk Hee
    • 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.

Analysis of Seoul Metropolitan Subway Network Characteristics Using Network Centrality Measures (네트워크 중심성 지표를 이용한 서울 수도권 지하철망 특성 분석)

  • Lee, Jeong Won;Lee, Kang Won
    • Journal of the Korean Society for Railway
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    • v.20 no.3
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    • pp.413-422
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    • 2017
  • In this study we investigate the importance of the subway station using network centrality measures. For centrality measures, we have used betweenness centrality, closeness centrality, and degree centrality. A new measure called weighted betweenness centrality is proposed, that combines both traditional betweenness centrality and passenger flow between stations. Through correlation analysis and power-law analysis of passenger flow on the Seoul metropolitan subway network, we have shown that weighted betweenness centrality is a meaningful and practical measure. We have also shown that passenger flow between any two stations follows a highly skewed power-law distribution.

Effect Analysis of an Additional Edge on Centrality and Ranking of Graph Using Computational Experiments (실험계산을 통한 에지 한 개 추가에 따른 그래프의 중심성 및 순위 변화 분석)

  • Han, Chi-Geun;Lee, Sang-Hoon
    • Journal of Internet Computing and Services
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    • v.16 no.5
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    • pp.39-47
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    • 2015
  • The centrality is calculated to describe the importance of a node in a graph and ranking is given according to the centrality for each node. There are many centrality measures and we use degree centrality, closeness centrality, eigenvector centrality, and betweenness centrality. In this paper, we analyze the effect of an additional edge of a graph on centrality and ranking through experimental computations. It is found that the effect of an additional edge on centrality and ranking of the nodes in the graph is different according to the graph structure using PCA. The results can be used for define the graph characteristics.

A Study on the Application to Network Analysis on the Importance of Author Keyword based on the Position of Keyword (학술논문의 저자키워드 출현순서에 따른 저자키워드 중요도 측정을 위한 네트워크 분석방법의 적용에 관한 연구)

  • Kwon, Sun-Young
    • Journal of the Korean Society for information Management
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    • v.31 no.2
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    • pp.121-142
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    • 2014
  • This study aims to investigate the importance of author keyword with analysis the position of author keyword in journal. In the first stage, an analysis was carried out on the position of author keyword. We examined the importance of author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality and effective size of structural hole. In the next stage, We performed analysis on correlation between network centrality measures and the position of author keyword. The result of correlation analysis on network centrality measures and the position of author keyword shows that there are the more significant areas of the result of the correlation analysis on degree centrality, betweenness centrality and the position of keyword. In addition, These results show that we need to consider that the possible way as measuring the importance of author keyword in journal is not only a term frequency but also degree centrality and betweenness centrality.

A Social Network Analysis of Research Key Words Related Smoke Cessation in South Korea (연결망 분석을 활용한 우리나라 금연연구 동향분석)

  • An, Eun-Seong
    • 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.

Internet Worm Propagation Model Using Centrality Theory

  • Kwon, Su-Kyung;Choi, Yoon-Ho;Baek, Hunki
    • Kyungpook Mathematical Journal
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    • v.56 no.4
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    • pp.1191-1205
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    • 2016
  • The emergence of various Internet worms, including the stand-alone Code Red worm that caused a distributed denial of service (DDoS), has prompted many studies on their propagation speed to minimize potential damages. Many studies, however, assume the same probabilities for initially infected nodes to infect each node during their propagation, which do not reflect accurate Internet worm propagation modelling. Thus, this paper analyzes how Internet worm propagation speed varies according to the number of vulnerable hosts directly connected to infected hosts as well as the link costs between infected and vulnerable hosts. A mathematical model based on centrality theory is proposed to analyze and simulate the effects of degree centrality values and closeness centrality values representing the connectivity of nodes in a large-scale network environment on Internet worm propagation speed.

An Analysis of Cultural Policy-related Studies' Trend in Korea using Semantic Network Analysis(2008-2017) (언어네트워크분석을 통한 국내 문화정책 연구동향 분석(2008-2017))

  • Park, Yang Woo
    • The Journal of the Korea Contents Association
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    • v.17 no.11
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    • pp.371-382
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    • 2017
  • This study aims to analyze the research trend of cultural policy-related papers based on 832 key words among 186 whole articles in the Journal of Cultural Policy by the Korea Culture & Tourism Institute from October 2008 to January 2017. The analysis was performed using a big data analysis technique called the Semantic Network Analysis. The Semantic Network Analysis consists of frequency analysis, density analysis, centrality analysis including degree centrality, betweenness centrality, and eigenvector centrality. Lastly, the study shows a figure visualizing the results of the centrality analysis through Netdraw program. The most frequently exposed key words were 'culture', 'cultural policy/administration', 'cultural industry/cultural content', 'policy', 'creative industry', in the order. The key word 'culture' was ranked as the first in all the analysis of degree centrality, betweenness centrality and eigenvector centrality, followed by 'policy' and 'cultural policy/administraion'. The key word 'cultural industry/cultural content' with very high frequency recorded high points in degree centrality and eigenvector centrality, but showed relatively low points in betweenness centrality.