• Journal of Navigation and Port Research
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• v.41 no.3
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• pp.155-164
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• 2017

While many countries are implementing various policies regarding the logistics network in Eurasia, China has presented "the Belt and Road" Initiative, a development strategy that focuses on connectivity and close cooperation between China and Eurasia. With more than 60 countries participating in the project, China is expected to have a major influence on logistical infrastructure development in Eurasia. This study analyzed the railway stations network using social network analysis (SNA) methodology. We collected data from major railway lines in Eurasia (TAR, TEN-T, TRACECA, GMS) and established a network of 994 railway stations in 65 countries. This study presented the general characteristics of major railway stations from the perspective of SNA and compared the Chinese network with Eurasian networks. To review the railway networks in China and Eurasia, the top 30 stations were selected based on degree centrality and betweenness centrality. Top "degree centrality" stations included Bangkok (Thailand), Tbilisi (Georgia), Baku (Azerbaijan), Kunming (China), and Bucharest (Romania). Top "betweenness centrality" stations were Baku and Alyat (Azerbaijan), Baoji and Turpan (China), Qarshi (Uzbekistan), and Kas (Turkey). In China, Kunming, Nanning, and Gejiu stations have higher degree centrality while betweenness centrality was higher in Baoji, Kunming, and Lanzhou stations. "The Belt and Road" project advocated by China envisions expansion of transportation infrastructure connections throughout Eurasia, but more emphasis is likely to be placed on connectivity that benefits China. In this regard, studies on key bases of international logistics need to consider relative significance within the Chinese network.

• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.79-95
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• 2016

This study identifies themes and trends in maritime economics and logistics by examining 303 papers published in international journals from 2000 to 2014 using keyword network analysis. Network analysis can be used because the collected data follow Zipf's law and the power law. Utilizing the degree centrality and betweenness centrality, we find the important keywords in each five year period and determine the importance of shared keywords. To further explain keyword centralities, we invented a Delta-C algorithm to show the trends of keywords over time. We found that degree centrality is useful for identifying important research themes in each period because it is mainly concerned with the number of connections. On the other hands, betweenness centrality is useful to determine the unique themes that emerge in each of the specific periods.

• Journal of Information Technology Services
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• v.18 no.4
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• pp.83-95
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• 2019

It is becoming more and more difficult to cope with new knowledge and technology required by society by the efforts of one person or organization according to the development of science and technology. As a method to overcome this, collaborative research is becoming important. This tendency is increasing in the government R&D projects as well, and the 'A' test research institute, which is the subject of this paper, is also increasing a collaborative research. The purpose of this study is to analyze the network characteristics among the participating researchers in the government R&D project conducted by the institution A, and to ascertain how the network characters of the researchers actually affect the financial performance of the team. The results of the analysis show that 'closeness centrality' and 'degree of centrality' contribute positively to the financial performance of the team. On the other hand, 'betweenness centrality' and 'eigenvector centrality' have a negative effect on the financial performance of the team because they are not directly related to financial performance.

• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.607-614
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• 2014

This article aims to explore and discuss research trends in global cruise industry using keyword network analysis. We visualize keyword networks in each of four groups of 1982-1999, 2000-2004, 2005-2009, 2010-2014 based on the top 20 keyword nodes' degree centrality and betweenness centrality which are selected among four centrality measurements, comparing them with frequency order. The article shows that keyword frequency collected from 240 articles published in international journals is subject to Zipf's law and nodes degree distribution also exhibits power law. We try to find out research trends in global cruise industry to change some important keywords diachronically, visualizing several networks focusing on the top two keywords, cruise and tourism, belonging to all the four year groups, with high degree and betweenness centrality values. Interestingly enough, a new node, China, connecting the top most keywords, appears in the most recent period of 2010-2014 when China has emerged as one of the rapid development countries in global cruise industry. Therefore keyword network analysis used in this article will be useful to understand research trends in global cruise industry because of increase and decrease of numbers of network types in different year groups and the visual connection between important nodes in giant components.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.582-591
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• 2015

Late-Koryeo people networks were constructed from four different history books that were written by various historic aspects in the period from king Kongmin to the final king of the Koryeo, Kongyang. All networks constructed in this study show scale free network properties as if most social networks do. Tajo-sillok preface is one of subjectively written history book that described personal history of the Lee Seong-gye and his ancestors. It is confirmed that the book is one of the most biased-written history books through network study. Jeong Do-jeon known as a Chosun dynasty projector is not greatly contributed for founding of a Chosun dynasty in network study and various historical documents as well. In this network study, we provide objective historical information in the historical situations of the late-Koryeo and during establishment procedure of Chosun dynasty. Hub nodes in network is denoted highly linked nodes, called degree. Stress centrality is a unit to measure positional importancy in the network. If we employ two factors, degree and stress centrality to determine hub node, it represents high connectivity and importancy as well. As comparing values of the degree and stress centrality, we elucidate more objective historical facts from late-Koryeo situations in this study. If we further develop and employ a new algorithm that is considered both degree and stress centrality, it is a very useful tool for determining hub node.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.2
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• pp.157-171
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• 2014

A organization of social enterprises is to achieve various goals such as private interests, the public nature, and social policy. For fulfilling these goals, we have to understand the various success factors. These success factors were shared among peoples. This study explored a position of structure of social network formed by success factors of Social Enterprises with importance. A position within social network defined a number of link connected other nodes. A position is closely associated with to individual's behaviors, opinions and thinking. We used social network analysis with two mode method for explaining feathers of structure of social network formed by success factors shared among peoples. We choose degree centrality for determining a position within social network. Centrality is a key measure in social network analysis. Results is that shared success factors are operation capital(15.15%) totally, and by Buying experience of products of Social Enterprises, Business Compliance(14.39%) and planning(12.88%), and by usage time of smart devices, Business Support(17.05%) and planning(16.10%). and the dominant success factor was not explored.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.49-65
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• 2015

As South Korean large business groups, also known as Chaebol, have broadened their influence in the domestic economy, it is important to analyze the influence of internal transactions among Chaebol affiliates on their performance. In this paper, relationship between internal transactions and efficiency of Chaebol affiliates has been analyzed. Top five Chaebol groups in South Korea are selected; they include Samsung, Hyundai Motors, LG, SK, and Lotte group. Based on internal transactions among affiliates, social networks are constructed for each Chaebol group to analyze centrality, network structures and cliques. Data Envelopment Analysis (DEA) was conducted to examine the efficiency of the Chaebol affiliates. Then, correlations between the degree centrality and the efficiency of Chaebol affiliates were analyzed, and the network structures of Chaebol groups are presented. The result shows that positive correlations between degree centrality and efficiency are observed among four Chaebol Groups. This paper shows that the Social Network Analysis (SNA) techniques can be used in the empirical research for the analysis of internal transactions of Chaebol groups.

• Journal of Korean Society for Quality Management
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• v.47 no.2
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• pp.315-325
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• 2019

Purpose: The purpose of this study was to propose useful standards evaluation method using standards networks by analyzing the relationship between normative references citation information list. Methods: The collected data through the survey were analyzed using social network analysis. The measurement tools used for this study were divided into three dimensions such as certificate standard, normative reference and degree centrality. Results: The results of this study are as follows; regarding the influence of standard information, It is meaningful that the research using normative reference item among the bibliography information that the standard itself possesses. The analysis of social network analysis data of population standard, normative reference and the correlation analysis with sales, a variable representing actual utilization performance, suggested the importance of the standard and how to evaluate it. Conclusion: In prioritizing standard maintenance, more efficient management will be possible if centrality figures of standard network information are analyzed and used for standard maintenance.

• Fashion & Textile Research Journal
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• v.20 no.5
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• pp.533-547
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• 2018

The objective of this study is to examine the perception of golfwear and related trends based on major keywords and associated words related to golfwear utilizing big data. For this study, the data was collected from blogs, Jisikin and Tips, news articles, and web $caf{\acute{e}}$ from two of the most commonly used search engines (Naver & Daum) containing the keywords, 'Golfwear' and 'Golf clothes'. For data collection, frequency and matrix data were extracted through Textom, from January 1, 2016 to December 31, 2017. From the matrix created by Textom, Degree centrality, Closeness centrality, Betweenness centrality, and Eigenvector centrality were calculated and analyzed by utilizing Netminer 4.0. As a result of analysis, it was found that the keyword 'brand' showed the highest rank in web visibility followed by 'woman', 'size', 'man', 'fashion', 'sports', 'price', 'store', 'discount', 'equipment' in the top 10 frequency rankings. For centrality calculations, only the top 30 keywords were included because the density was extremely high due to high frequency of the co-occurring keywords. The results of centrality calculations showed that the keywords on top of the rankings were similar to the frequency of the raw data. When the frequency was adjusted by subtracting 100 and 500 words, it showed different results as the low-ranking keywords such as J. Lindberg in the frequency analysis ranked high along with changes in the rankings of all centrality calculations. Such findings of this study will provide basis for marketing strategies and ways to increase awareness and web visibility for Golfwear brands.

• 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%.