• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.77-85
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• 2013

This paper proposes a closeness centrality analysis algorithm for workflow-supported social networks that represent the collaborative relationships among the performers who are involved in a specific workflow model. The proposed algorithm uses the social network analysis techniques, particularly closeness centrality equations, to analyze the closeness centrality of the workflow-supported social network. Additionally, through an example we try to verify the accuracy and appropriateness of the proposed algorithm.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.47-53
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• 2016

A type of workflow affiliation network is one of the specialized social network types, which represents the associative relation between actors and activities. There are many methods on a workflow affiliation network measuring centralities such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In particular, we are interested in the closeness centrality measurements on a workflow affiliation network discovered from enterprise workflow models, and we know that the time complexity problem is raised according to increasing the size of the workflow affiliation network. This paper proposes an estimated ranking algorithm and analyzes the accuracy and average computation time of the proposed algorithm. As a result, we show that the accuracy improves 47.5%, 29.44% in the sizes of network and the rates of samples, respectively. Also the estimated ranking algorithm's average computation time improves more than 82.40%, comparison with the original algorithm, when the network size is 2400, sampling rate is 30%.

• 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 Korea Convergence Society
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• v.9 no.9
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• pp.9-14
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• 2018

This study aims to investigate an importance of Author keyword with analysis the position of author keyword. An analysis was carried out on the position of author keyword. we examined an importance of Author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In the next stage, we performed analysis on correlation between network centrality measures and the position of keyword. As a result, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality both has a high value in 4th author keyword order. eigenvector centrality was the comparatively effective method to separate of author keyword order method than other 3 centrality. Correlation analysis result shows that the network analysis value are increasing in order. This study has significance in that it was able to examine the author keyword behavior. Future research is needed to identify and supplement future situational factors, behavior, and psychology.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.29-50
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• 2016

We explore the impact of individuals'network position toward knowledge sharing intention. In order to identify network positions, we utilize three centrality measures (degree/closeness/betweenness) of individual network participants. The research findings show that the individual network positions significantly affect knowledge sharing intentions. Since an individual with high degree centrality might be the leader or the hub, one makes considerable effort to maintain the network position by actively participating in intra-team and inter-team knowledge sharing, A participant who can quickly interact with many other participants within a team (high closeness centrality) is more interested in intra-team knowledge sharing than inter-team knowledge sharing. Unlike degree centrality and closeness centrality, the betweenness centrality provides a participant with diverse resources located in multiple sub-groups. Although an individual with high betweenness centrality is not at the center of the networks, one plays a crucial role in disseminating and regulating information. Therefore, the individual is likely to have more positive intention toward inter-team knowledge sharing than intra-team knowledge sharing.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.314-315
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• 2018

본 연구는 사회연결망 분석을 활용하여 2012년부터 2016년까지 부산항을 기항하는 컨테이너 정기선 항로 패턴 분석을 통해 세계 주요항만과의 중심성을 파악하여 부산항과 연결되어 있는 항만 네트워크의 구조적인 특성을 파악하였다. 부산항 컨테이너 정기선 항로 네트워크상에 연결정도 중심성, 근접중심성, 매개중심성이 높은 항만은 싱가포르항으로 분석되었으며, 실제 연도별 부산항 주요 국가 항만별 처리 물동량 순위와 부산항 컨테이너 정기선 항로 네트워크 중심성 분석 결과 간 순위 비교는 서로 상이한 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.401-409
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• 2011

From a social network of n nodes connected by l lines, one may produce centrality measures such as closeness, betweenness and so on. In the past, the magnitude of n was around 1,000 or 10,000 at most. Nowadays, some networks have 10,000, 100,000 or even more than that. Thus, the scalability issue needs the attention of researchers. In this short paper, we explore random networks of the size around n = 100,000 by Monte-Carlo method and propose Monte-Carlo algorithms of computing closeness and betweenness centrality measures to study the small world properties of social networks.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.2
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• pp.99-117
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• 2017

The present study positively analyzed the hyperlinks of 32 web sites with the purpose of analyzing the hyperlink network structure of web sites for each domestic library type. After collecting the hyperlink data using the crawler, we analyzed the overall characteristics of the websites in the network based on the characteristics of the library. The results are as follows. 1) Among all analyzed libraries, Yonsei scored the highest in degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. 2) By library type, Sejong for national library, Seoul for public library, and Yonsei for college library appeared an influential a relatively. Based on these analysis results, the present study will be utilized as basic data for establishing an operation strategy that improves the efficiency and effectiveness of library web sites in the future.

• 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.

• Spatial Information Research
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• v.23 no.1
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• pp.69-77
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• 2015

The aim of this study is to examine the research trends of global scientific production of Geospatial Information (GI) papers from 1998 to 2013 by using keyword network analysis. This study constructed keyword network model through papers and keywords related to GI research retrieved from the Web of Science DB and performed keyword network analysis such as Degree Centrality, Betweenness Centrality, and Closeness Centrality. The results show that GI has been steadily applied to various fields, and also the research trends of GI techniques could be quantitatively characterized through keyword network analysis. This study result can be applied to establish the policies and the national R&D planning of geospatial information.