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

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.2
• /
• pp.447-452
• /
• 2019

The elder problem is very serious stage in the age of present. This paper carries out scientometric analysis based on keyword that effort of global researcher for this research field as viewpoint of ICT and healthcare in order to settle physical exercise and rehabilitation of elder. First, this paper performs analysis of linkage relation of keyword. Second this paper carries out the analysis of degree distribution and centrality analysis of network based on betweenness centrality, closeness centrality and harmony centrality. Through this process, this paper reviews research trend of present and future through core keyword in the field of ICT and healthcare.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.18 no.3
• /
• pp.9-23
• /
• 2022

This study aims to examine research trends on the Internet of Things and smart city based on papers from the United States, Japan, and Korea. We collected 7113 papers related to the Internet of Things and smart city published from 2016 to 2021 in Elsevier's Scopus. Keyword frequency and centrality analysis were performed based on the abstracts of the collected papers. We found keywords with high frequency of appearance by calculating keyword frequency and identified central research keywords through the centrality analysis by country. As a result of the analysis, research on security, machine learning, and edge computing related to the Internet of Things and smart city were the most central and highly mediating research conducted in each country. As an implication, studies related to deep learning, cybersecurity, and edge computing in Korea have lower degree centrality and betweenness centrality compared to the United States and Japan. To solve the problem it is necessary to combine these studies with various fields. The future research direction is to analyze research trends on the Internet of Things and smart city in various regions such as Europe and China.

• Korea Trade Review
• /
• v.47 no.6
• /
• pp.189-209
• /
• 2022

This study aims to analyze Latin America and the Caribbean papers published in Korea during the past 2000-2020 years. Through this study, it is possible to understand the main subject and direction of research in Korea's Latin America and the Caribbean area. As the research mythologies, this study uses the text mining and Social Network Analysis such as frequency analysis, several centrality analyses, and topic analysis. After analyzing the empirical results, there has been a tendency to change the key words and centrality coefficients between 2000-2010 and 2011-2020 years. During 2011-2020 years, the most frequent keywords were changed from Neoliberalism and culture to policy education, and economy related words. The degree and closeness centrality analyses appeared the higher frequency key words. However, the eigenvector centrality appeared very different from the order of frequency key words. The topic analysis shows that the culture, language, and Neoliberalism were the most important keywords during 2000-2010 years but economy, labor trade, industry, development became the most important keywords during 2011-2020 years in topics.

• Asia pacific journal of information systems
• /
• v.29 no.1
• /
• pp.50-64
• /
• 2019

The success of Wikipedia is due to the large number of volunteers collaborating to provide content to Wikipedia articles. In this paper, we study the effect of network position on the promotion of an article to a featured article. We focus on the edits of featured Wikipedia articles to study the effects of the centrality of editors and centrality of articles on the promotion of featured articles. Considering the editing precedence among editors within an article, editor collaboration networks for a single article are generated using the total English-written featured articles on Wikipedia. In addition, based on the affiliation network of editors and articles, an article-to-article network and an editor-to-editor collaboration network are constructed. Based on the investigation of the networks, we find that article centrality in an article-to-article network has a negative effect on the promotion of an article, and editor centrality in an article-to-article networks has a positive effect on promotion. In addition, editor centrality in an editor-to-editor network has a negative effect on promotion. Some theoretical and managerial implications are provided in view of these results.

• Fashion & Textile Research Journal
• /
• v.20 no.5
• /
• pp.533-547
• /
• 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
• /
• v.17 no.1
• /
• pp.47-53
• /
• 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%.

• The Journal of the Korea Contents Association
• /
• v.13 no.11
• /
• pp.894-901
• /
• 2013

This study is aim to research trend of private security and to suggest direction of improvement to sector of private security. This study has been to analyzed to be useful social network analysis(particularly Degree Centrality and Clossness Centrality) for using typical research method about trend of academic subject. As a result of Degree Centrality and Clossness Centrality, Individual factors such as Job Stress and Job satisfaction of private security are more keyword than institutional factors and policy factors such as Security Services Industry Act and training for Private Security guards. It means that research trend of private security are to study Individual factors rather than institutional factors and policy factors. But, this study is a limit as follows; First, An object of study is only to searching article in National Assembly Liberary. A follow-up studies are need to expand the range of an object of study for private security.

• Korean Journal of Ecology and Environment
• /
• v.50 no.4
• /
• pp.478-482
• /
• 2017

In order to identify key nations and bird species of conservation concern we described multinational collaborations as defined using network analysis linked by birds that are found in all nations in the network. We used network analysis to assess the patterns in bird occurrence for 10,422 bird inventories from 244 countries and territories. Nations that are important in multinational collaborations for bird conservation were assessed using the centrality measures, closeness and betweenness centrality. Countries important for the multinational collaboration of bird conservation were examined based on their centrality measures, which included closeness and betweenness centralities. Comparatively, the co-occurrence network was divided into four groups that reveal different biogeographical structures. A group with higher closeness centrality included countries in southern Africa and had the potential to affect species in many other countries. Birds in countries in Asia, Australia and the South Pacific that are important to the cohesiveness of the global network had a higher score of betweenness centrality. Countries that had higher numbers of bird species and more extensively distributed bird species had higher centrality scores; in these countries, birds may act as excellent indicators of trends in the co-occurrence bird network. For effective bird conservation in the world, much stronger coordination among countries is required. Bird co-occurrence patterns can provide a suitable and powerful framework for understanding the complexity of co-occurrence patterns and consequences for multinational collaborations on bird conservation.

• Journal of Information Technology Services
• /
• v.16 no.3
• /
• pp.167-183
• /
• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.