• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.97-122
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• 2016

Incheon port attained two million TEU of container throughput between 2013 and 2014 as a third port in domestic container throughput. It opened a new port in Song-do, Incheon in June 2015 to prepare for the continuing increase in container throughput.Therefore, it has provided the platform for being the major container port domestically and internationally. As the role of the new port increases, the role and direction of the Incheon port liner service network attracts attention. This study analyzes the centrality of the Incheon port liner service network by using SNA (Social Network Analysis), which was introduced in the maritime economics area recently, focusing on the Incheon port liner service network. We recognize the degree centrality, closeness centrality, and betweenness centrality of each port and its effect on the Incheon port liner service network. The study showed that for Incheon port, the centrality of the Busan port in Korea, and the Hong Kong port, is high outside the country. This helps us determine that the hub of the Incheon port is neither Shanghai nor Singapore, which ranks first and second, respectively, on container throughput. It is also helps us to know that eastern China's ports have not played a role of the hub of the Incheon port until now because of the relatively low centrality of eastern China's ports.

• The Journal of Korean Association of Computer Education
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• v.17 no.4
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• pp.69-78
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• 2014

This study aims to investigate better predictors, among learner participation and interaction, for collective self-efficacy and achievement in a web-based collaborative learning environment. Interaction requires communication among two or more learners, while participation does not. In this study, interaction was measured by in-degree centrality and out-degree centrality based on the social network analysis perspective. Multiple regression analysis results from 53 college students who performed team project via online showed that in-degree centrality predicted collective self-efficacy and out-degree centrality predicted achievement, while participation was not a significant predictor.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.5
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• pp.1-15
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• 2017

In this study, we visualized the regional migration in Korea from 2001 to 2015 using the Chord diagram which can represents amount of migration and flows at the same time. In addition, we constructed a migration network and analyzed the degree centrality of each region for identifying the main regions linking to various regions. In 2001~2005, most of population moved into Geonggi from various regions. However, the capital function was transferred to Sejong in 2011~2015, and population moving into Sejong and Chungnam was increased significantly. The main outflow of population in migration network were shown at the regions in Jeonbuk and Gyeongbuk province in 2001~2004, and recently the regions in Gyeongnam, Gyeonggi, and Seoul were identified as the main nodes in terms of outflow of population. We also focused on migration in rural area through degree centrality, and cord diagram in Chungnam, Jeonbuk, and Jeonnam where include the representative crop area. In 2015. there was the significant increase of migration from Gyeonggi to Chungnam, and internal migration within Jeonbuk increased rather than cross-border migration. In addition, migration from Jeonam to capital area decreased in 2015 but migration among cities within Jeonman increased. In particular, Yesan-gun showed the significant migration to other cities in Jeonnam. Population is necessary to develop community and sustain economic growth in rural regions. Therefore, migration is important for the transfer of manpower. The strength of this study is to approach the temporal change of migration from the viewpoint on quantitative and structural characteristics.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• Journal of the Korean Society for information Management
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• v.34 no.3
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• pp.49-66
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• 2017

The aim of this study was to analyze the research trends on the digital libraries in Korea. To achieve this objective, a total of 578 author-assigned English keywords were collected from the 272 major LIS journal articles published in Korea during last ten years-period, i.e., 2007-2016. The collected data were analyzed using NetMiner V.4 to discover their 'degree centrality' and 'betweenness centrality'. As the results, 'Academic Library', 'Reference Service', 'Public Library', 'E Resource', and 'E Book' showed the most frequently conducted research topics, and 'Academic Library', 'Reference Service', 'Information Behavior', and 'E Resource' were the most influencing research topics. Finally, 'Academic Library', 'Metadata', 'Information Behavior', 'E Resource', and 'Librarian' seemed the most widely intervening research topics in this research.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.35-46
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• 2011

As development of exhibition, it has been an important issue to analyze exhibition for the next success of exhibition. A lot of existing researches have focused on the exhibitor's and visitor's satisfaction problem. However, the exhibitor's satisfaction and success of exhibition come from the analysis of exhibition in the network level. Booths composing exhibition are regarded as nodes in network, so the trace of visitors visiting booths can construct the arc of networks. The purpose of this study is to analyze the booth visiting pattern and components of network through social network analysis using data collected in the $17^{th}$ International KIDS & EDU EXPO for Children. This research is the first approach of network-leveled analysis of exhibition, and the result of network analysis is helpful to support the booth arrangement in next exhibition. Our analysis results the following implications. First, Booths with high degree centrality or betweenness centrality should be deployed in the wide space or corner of the exhibition hall. Finally, booths within a block should be deployed in the same space of the exhibition hall to provide convenience to the visitors and to enhance exhibition performance.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.153-158
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• 2018

The visitors' patterns of the homepages in university were classified and analyzed with the network analysis based on the hyperlinks. The numbers of visits to English web-pages were proportional to those of Korean with much less counts. The larger count of visits was confirmed for the case of colleges than the departments, showing the upper boundary of visits from the plot with the Betweenness centrality normalized by the degree. For the better visibility, well-designed hyperlinks with the proper public relations were suggested based on the quantitative analysis of visitors' count.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.89-103
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• 2022

The purpose of this study is to explore professors' perception of blended learning. For this purpose, the reflective journals written by 56 university professors was analyzed using the keyword network analysis method. The results of this study are as follows: First, as a result of keyword frequency analysis for the blended learning, the keywords showed the highest frequency in the order of (1) 'instructional design', 'student', 'instructional method', 'learning objective' in the area of learning, (2) 'importance', 'instruction', 'feeling', 'student' in the area of feeling, and (3) 'semester', 'plan', 'weekly', and 'instruction' in the area of action plan. Second, the results of analyzing the degree, closeness centrality, and betweenness centrality of network connection are as follows. (1) The keywords 'instruction', 'instructional method', 'instructional design', and 'learning objective' in the area of learning, (2) the keywords 'instruction', 'importance', and 'necessity' in the area of feeling, and (3) 'instruction', 'plan', and 'semester' in the area of action plan showed high values in degree, closeness centrality, and betweenness centrality. Based on the research results, implications for blended learning and professors' perception were discussed.

• 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.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.387-399
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• 2015

Recently collaboration network is generated to find out experts in their field as potential collaborators in health care sector. In this paper, the co-author network of a National Cancer Center researcher was generated for identifying each researcher's role and collaborative research pattern. The co-author network of 2,437 authors was extracted from 1,194 SCI(E) publications from 2000 to 2010 and author's role was analyzed by author's centrality value. Centrality reflecting only the number of papers and centrality weighted by the paper number, impact factor, and authorship contribution was evaluated. On the comparison with simple degree centrality value and the weighted degree centrality, difference of value was statistically significant(t=11.66, p=0.00). Co-author network considering various variables of the paper provides more objective figure of researcher's role. This suggests that co-author network could be more effective in identifying potential collaborators.