• Journal of the Korean Society for information Management
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• v.31 no.3
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• pp.153-179
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• 2014

This study explores the characteristics of centrality measures for analyzing researchers' impact and structural positions in research collaboration networks. We investigate four binary network centrality measures (degree centrality, closeness centrality, betweenness centrality, and PageRank), and seven existing weighted network centrality measures (triangle betweenness centrality, mean association, weighted PageRank, collaboration h-index, collaboration hs-index, complex degree centrality, and c-index) for research collaboration networks. And we propose SSR, which is a new weighted centrality measure for collaboration networks. Using research collaboration data from three different research domains including architecture, library and information science, and marketing, the above twelve centrality measures are calculated and compared each other. Results indicate that the weighted network centrality measures are needed to consider collaboration strength as well as collaboration range in research collaboration networks. We also recommend that when considering both collaboration strength and range, it is appropriate to apply triangle betweenness centrality and SSR to investigate global centrality and local centrality in collaboration networks.

• Smart Media Journal
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• v.11 no.8
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• pp.73-83
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• 2022

In order to support new construction engineering companies with weak information power to successfully advance into the overseas construction market, this study tried to analyze what are the keywords of interest in the overseas construction market and how they differ from Korea. For this purpose, we recently collected 2,473 news article titles and major articles targeting smart cities that are of high interest in Korea and Vietnam. Through network configuration and topic modeling, we examined the connection relationship between the word of interest and the word of interest. In addition, the influence of the word of interest in the network was measured using PageRank centrality. Through this analysis, it was found that there is a high interest in smart city-related construction, cities, and digital in both countries, and the difference in terms of interest between Korea and Vietnam was inferred. Finally, the limitations of this study and additional research directions to complement them are presented.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.71-86
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• 2021

This study was conducted to find information on the university's image from words related to 'Korea National College of Agriculture and Fisheries (KNCAF)' in Naver News. For this purpose, word frequency analysis, TF-IDF evaluation and semantic network analysis were performed using web crawling technology. In word frequency analysis, 'agriculture', 'education', 'support', 'farmer', 'youth', 'university', 'business', 'rural', 'CEO' were important words. In the TF-IDF evaluation, the key words were 'farmer', 'dron', 'agricultural and livestock food department', 'Jeonbuk', 'young farmer', 'agriculture', 'Chonju', 'university', 'device', 'spreading'. In the semantic network analysis, the Bigrams showed high correlations in the order of 'youth' - 'farmer', 'digital' - 'agriculture', 'farming' - 'settlement', 'agriculture' - 'rural', 'digital' - 'turnover'. As a result of evaluating the importance of keywords as five central index, 'agriculture' ranked first. And the keywords in the second place of the centrality index were 'farmers' (Cc, Cb), 'education' (Cd, Cp) and 'future' (Ce). The sperman's rank correlation coefficient by centrality index showed the most similar rank between Degree centrality and Pagerank centrality. The KNCAF articles of Naver News were used as important words such as 'agriculture', 'education', 'support', 'farmer', 'youth' in terms of word frequency. However, in the evaluation including document frequency, the words such as 'farmer', 'dron', 'Ministry of Agriculture, Food and Rural Affairs', 'Jeonbuk', and 'young farmers' were found to be key words. The centrality analysis considering the network connectivity between words was suitable for evaluation by Cd and Cp. And the words with strong centrality were 'agriculture', 'education', 'future', 'farmer', 'digital', 'support', 'utilization'.

• Proceedings of the Korean Society for Information Management Conference
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• 2013.08a
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• pp.27-30
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• 2013

공동연구 네트워크의 대표적인 사례인 공저 네트워크는 오랫동안 네트워크 분석의 대상으로 다루어져 왔다. 최근에는 가중 네트워크로서 공저 네트워크에 대한 연구가 활발해지면서 연구자의 영향력을 측정하려는 몇 가지 척도가 제안되었다. 이 연구에서는 공저 네트워크에서의 중심성을 측정하기 위해서 사용된 척도인 가중페이지랭크, 공동연구 h-지수와 공동연구 hs-지수, 복합연결정도중심성, c-지수에 대해서 비교 분석해본다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.171-174
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• 2019

본 논문에서는 사회연결망 분석을 이용하여 군 장비정비정보체계의 정비데이터를 분석하고 이를 통해 정비 분야 전문가를 파악하고자 하였다. 장비정비정보체계는 군에서 장비를 효율적으로 정비하고 관리하기 위해 2009년부터 운용하고 있는 체계로 해군한 정비부대에 대한 2017년 정비데이터(00,000건)의 일부(0,000건)를 페이지랭크 중심성 분석을 통해 정비 분업화 수준과 참여도를 확인함으로써 전문분야를 확인하였다.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.255-256
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• 2023

본 연구에서는 코로나19로 인하여 전국적으로 전례 없는 통행체계 위축 등 통행행태 변화가 발생되어 수도권 및 인천시 지역적 세부 통행체계에 대하여 네트워크 중심성 분석을 통하여 지역별 교통 거점과 외부 영향에 따른 지역적 교통 거점 변화를 비교 분석하였다. 또한, 대중교통 이용통행 분석 결과, 코로나19 유행 시기별로 통행량이 크게 감소됨에 따라 버스, 지하철 등 수단별 대책 마련하는데 기여할 것으로 판단된다.

• Journal of Technology Innovation
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• v.27 no.4
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• pp.1-19
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• 2019

This study presents a new method to evaluate patents by public R&D program using patent citation network analysis. I used forward citation, degree centrality, betweenness centrality and page rank as the dependent variables which represents the quality of patents. I used primary independent variable as a dummy of public R&D program and controlled patents characteristics, applicant characteristics, technological characteristics and year effect. The empirical result shows that the patents of public R&D program is superior to other patents in regard to the number of forward citation, the degree centrality, the betweenness centrality and the page rank. This empirical result implies that patents of public R&D program directly and effectively connects technologies. Also patents from public R&D program connects important technologies.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).

• Information Systems Review
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• v.13 no.1
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• pp.75-96
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• 2011

This research uses coolhunting to predict the future trend of e-Commerce industry. Coolhunting is a method to take Cool Trends which are the future trend through social network analysis for discovering the trendsetter and its collective intelligence. Coolhunting is generally carried out by social network analysis while this research uses NodeXL of social network analysis tools. We designed industrial network research model for relation among e-Commerce corporation, product, the types of customer service and customer service employee to discover the Cool Trends of e-Commerce industry. According to the result of this research, e-Commerce industrial network was being changed from chaos to collective intelligence form. As a analysis result for network influences, we found that Cool Trends of e-Commerce industry invigorate social commerce industry through the collective intelligence focusing intelligence VIP, Excellence, grade of Administrating for women customers(trendsetter) and it promotes semantic consumption from customers and purchasing power will be concentrated on cosmetic, beauty, perfume product categories in social commerce. We propose the strategic direction for e-Commerce corporation and hope that domestic e-Commerce corporation continues to grow and high-quality services are provided for customers.

• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.69-93
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• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).