• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.

• Journal of Science Education
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• v.40 no.3
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• pp.267-286
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• 2016

The study aimed to investigate a research trend in science gifted education of six years from 2010 to 2015 by utilizing literature analysis and Social Network Analysis (SNA) methods. In this study, 275 papers published in eight major academic journals of science education and gifted education were selected as research subjects. First, through the literature analysis, it was found that the most frequent research topics were cognitive characteristics (25.8%), curriculum/programs (22.6%), and social and emotional characteristics (20.2%). For the research method employed in research papers, the survey research (46.5%) was appeared as the most frequently employed method, and followed by experimental (18.8%), program development (10.6%), correlation (10.3%), and qualitative (6.4%) research methods. The most frequent research subject was appeared as middle school students (33.7%) and followed by elementary school (30.6%), and high school (12.7%) students. Second, the SNA method was utilized for producing keyword frequency, degree centrality and network analyses. It was appeared that the most common keywords over six years included 'science gifted', 'gifted education', and 'creativity' and frequent keywords were science gifted, gifted education, gifted, creativity, science inquiry, perception, (creative) problem solving, science high school, scientific attitude, and STEAM. Third, through 2-mode network analysis, it was found that the research papers about cognitive characteristics were mainly related to perceptions, thinking ability, scientific argumentation, science inquiry and so on. It was also found that the research papers about social and emotional characteristics were related to correlation, motivation, creativity-character, self-efficiency and so on. It was concluded that the SNA method can be performed with literature analysis together for better understandings and interpretations of the research trend of science gifted education in-depth.

• Journal of Korea Technology Innovation Society
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• v.17 no.1
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• pp.242-263
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• 2014

Establishment of efficient cooperative ecosystem of research institutes is important for the efficiency of national innovation system, especially in the era of technology convergence. Performance of institutes inside the ecosystem is dependent on the position of the institutes in the system. This study investigates the relationship between network structure and research performance, and determines significant factors on the research performance. The results of 5 year panel data analysis of SCI journal papers of Korean government research institutes indicate that four network centralities -degree, betweenness, closeness, and eigenvector- and structural holes have significant effect on the research performance of the institutes. Among the four centralities, closeness and eigenvectors are more significant than others. Implications of the results of this study for policy of establishing efficient cooperative system are that increasing the cooperative activities of less active institutes is more effective for research performance than increasing the magnitude of cooperative activities of all institutes. Also, when an institute starts a new cooperative relationship, it is better to have relationship with an active institute first.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.23-41
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• 2019

This study understand the subject domain of book which has been read by men and woman elderly by analizying the PFNET using library big data and confirm the difference between adult at age 30-40. This study extract co-occurrence matrix of book lending on the popular book list from library big data, for 4 group, men/woman elderly, men/woman adult. With these matrix, this study performs FP network analysis. And Pearson Correlation Analysis based on the Triangle Betweenness Centrality calculated on the loan book was performed to understand the correlation among the 4 clusters which has been created by PNNC algorithm. As a result, reading trend which has been focused on modern korean novel has been revealed in elderly regardless gender, among them, men elderly show extreme tendency concentrated on modern korean long series novel. In the correlation analysis, the male elderly showed a weak negative correlation with the adult male of r = -0.222, and the negative direction of all the other groups showed that the tendency of male elderly's loan book was opposite.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.23-46
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• 2016

With the increasing socio-economic value of an open collaboration over the Internet, it has become significantly important to successfully manage open source software development program. Most of the previous research have focused on various factors that influence the performance of the project, but studies on how the project owners recognized as "leader" affect the outcome of the project are very limited. This research investigates how individual and governance characteristics of an owner influences the performance of project based on impression formation and social capital theory. For a data set, we collect 611 Repositories and the owner's data from the open source development platform Github, and we form knowledge sharing network of an each repository by using social network analysis. We use hierarchical regression analysis, and our results show that a leader, who exposes a lot of one's personal information or who has actively followed and showed interests to communicate with other developers, affects positive impacts on project performance. A leader who has a high centrality in knowledge sharing network also positively affects on project performance. On the other hand, if a leader was highly willing to accept external knowledge or is recognized as an expert in the community with large numbers of followers, the result show negative impacts on project performance. The research may serve as a useful guideline not only for the future open source software projects but also for the effective management of different types of open collaboration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.728-739
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• 2020

Using webometric big data, this study examines the diversity of online information sources about the novel coronavirus causing the COVID-19 pandemic. Specifically, it focuses on some 28 countries where confirmed coronavirus cases occurred in February 2020. In the results, the online visibility of Australia, Canada, and Italy was the highest, based on their producing the most relevant information. There was a statistically significant correlation between the hit counts per country and the frequency of visiting the domains that act as information channels. Interestingly, Japan, China, and Singapore, which had a large number of confirmed cases at that time, were providing web data related to the novel coronavirus. Online sources were classified using an N-tuple helix model. The results showed that government agencies were the largest supplier of coronavirus information in cyberspace. Furthermore, the two-mode network technique revealed that media companies, university hospitals, and public healthcare centers had taken a positive attitude towards online circulation of coronavirus research and epidemic prevention information. However, semantic network analysis showed that health, school, home, and public had high centrality values. This means that people were concerned not only about personal prevention rules caused by the coronavirus outbreak, but also about response plans caused by life inconveniences and operational obstacles.

• Korean Journal of Legislative Studies
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• v.24 no.2
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• pp.67-99
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• 2018

This study analyzes the revision bills of the National Assembly Law in the 19th National Assembly in which the National Assembly Advancement Act was enacted, with the question "who are involved in the revision of the procedural rules, and what motivates them?" The cosponsor network analysis focusing on primary sponsors of the revision bills shows that the network was constructed by party affiliations. A small number of members with high degree centrality attempted to cooperate with each other at the cosponsoring stage, but the legislation did not pass through the related committee. In addition, this study tests the four competitive hypotheses (the committee hypothesis, the distributive politics hypothesis, the ideological distance hypothesis, and the partisan affiliation hypothesis) about the motivation to propose amendments by using the regression models which include newly measured variables. Only the committee hypothesis and the partisan affiliation hypothesis are empirically supported. This implies that partisan consideration is still significant in amending the National Assembly Law even after the National Assembly Advancement Act, and thus party leaders' willingness to seek bipartisan compromises is at the heart of problem-solving.

• Korean Journal of School Psychology
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• v.18 no.1
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• pp.31-48
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• 2021

The purpose of this study was to determine whether the retention of university freshmen could be predicted using their peer relationships in a specific department. In this study, retention was defined as a student staying enrolled in their university for a certain period of time. Social relationships are formed through interaction between people, so both students' self-perceptions and others' perceptions of them must be accounted for, so we used a social network analysis that did so. We examined social networks visualizations that allowed for a rich interpretation of numerical information. Participants in this study were freshmen who enrolled in an undergraduate program in 2017, 2018, or 2019. We used the name generator method to determine how quantitative friendship network variables predicted the academic retention up to the first semester of 2020. Cox proportional hazard model analysis showed that the weighted indegree centrality with intimacy positively predicted retention. The results of this study can be used to identify and conduct interventions for students who may be likely to disenroll. However all of the students did not participate in the department, it was difficult to examine their entire peer networks. Thus, this study's results cannot be generalized because the participants are students of a specific major, so further research is needed to produce more generalizable results.

• Journal of Internet of Things and Convergence
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• v.3 no.2
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• pp.33-39
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• 2017

The purpose of this study is to analyze the characteristics of talents required in electronic companies in the 4th industrial revolution. we conducted a network analysis on the key talent of the companies presented in over 100 job announcements to companies in the electronics industry. The results of the study are as follows. First, electronic companies showed the most favored creative talents, preferring collaborative talent and challenging talent. Second, looking at the core definitions of talent, change, response, problem solving, performance creation, communication, challenge, professionalism, enthusiasm, development, aggressiveness and spontaneity were used. In other words, key keywords emerging from the 4^th industrial revolution were being used continuously. Third, in the Centrality analysis, talented people who emphasize humanity also appeared. Based on the study, it suggested that manpower training of the 4^th Industrial Revolution.