• Asian Journal of Innovation and Policy
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• v.6 no.1
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• pp.85-93
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• 2017

This paper focuses on the relationship between the characteristics of network and the productivity of scientists, which is rarely examined in previous studies. Utilizing a unique dataset from the Korean Citation Index (KCI), we examine the overall characteristics of the research network (e.g. distribution of nodes, density and mean distance), and analyze whether the network centrality is related to the scientific productivity. According to the results, firstly we have found that the collaborative research network of the Korean academics in the field of statistics and computer science is a scale-free network. Secondly, these research networks show a disciplinary difference. The network of statisticians is denser than that of computer scientists. In addition, computer scientists are located in a fragmented network compared to statisticians. Thirdly, with regard to the relationship between the researchers' network position and scientific productivity, a significant relation and their disciplinary difference have been observed. In particular, the degree centrality is the strongest predictor for the scientists' productivity. Based on these findings, some policy implications are put forward.

• Ocean and Polar Research
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• v.38 no.3
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• pp.209-224
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• 2016

The purpose of this study is to analyze research activities related to Antarctic science through a bibliographic study and to understand and evaluate the implications. This study is based on 78,445 articles which were retrieved from the Science Citation Index(SCI) database during the period 1998-2015. Through a quantitative analysis and a Social Network Analysis, we made several findings and drew out the implications. First, many countries, in general, have increased multi-national research cooperation in order to enhance research productivity. However, Korea's cooperative research activity is below the average level. Second, considering the 4 centrality indexes, which are derived from the SNA, Korea had a lower score in terms of centrality indexes. Based on these findings, Korea should formulate a more dynamic or proactive strategy in order to enhance its participation in international research cooperation efforts. Korea, the 10th country to build two or more research bases in Antarctica, should make greater efforts to bring the appropriate level of the phase.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.361-368
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• 2004

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within an Computer Supported Corporative Learning (CSCL) environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order learning performance and the peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.

• Journal of Korean Society of Transportation
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• v.35 no.6
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• pp.525-544
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• 2017

With the advent of the big data era, the interest in the development of land using traffic data has increased significantly. However, the current research on traffic big data lingers around organizing or calibrating the data only. In this research, a novel method for discovering the hidden values within the traffic data through data mining is proposed. Considering the fact that traffic data and network structures have similarities, network analysis algorithms are used to find valuable information in the actual traffic volume data. The PageRank and HITS algorithms are then employed to find the centralities. While conventional methods present centralities based on uncomplicated traffic volume data, the proposed method provides more reasonable centrality locations through network analysis. Since the centrality locations that we have found carry detailed spatiotemporal characteristics, such information can be used as an objective basis for making policy decisions.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.127-140
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• 2015

In this study, we aim to analyze the relationship between the centrality in the social network and the sentiment of medial web forum users. In recent, many people use online resources to obtain health and wellness information especially social media resources. In the medial web forum, people give and receive informational supports and emotional supports and this interaction forms the social network. We analyze the social network, derive node characteristics in terms of centrality and compare the centrality index and the sentiment score derived from users' messages. We found that as more people express their emotion, they possess higher central position in the network. Further, people who express positive emotion in their messages have higher central position in the network than people who have negative emotion. This study will help to identify influentials of emotional supports to others and finally to control the depression of Alzheimer's disease patients and their related ones.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.2
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• pp.173-192
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• 2021

The purpose of this study is to analyze the previous research on geological fieldwork from 2000 to 2020, examine the tasks that have been focused on, and suggest directions and implications for future geological fieldwork research. The data was conducted for the thesis searched on ScienceON and RISS in relation to geological fieldwork and journals listed in the Korean Citation Index(KCI), and the study title was analyzed using the semantic network analysis. For analysis, the data that had been pre-processed was visualized as a network by semantic network analysis, and frequency and centrality were analyzed. The centrality analysis was based on degree centrality and eigenvector centrality, and all analyzes were performed by dividing the entire study period into four periods: 2000-2005, 2006-2010, 2011-2015, and 2016-2020. As a result, research on geological fieldwork focused more on the development of geological field courses, and in particular, jeju island was actively discussed as a learning site. Also, the study was conducted on students rather than teachers, and among them, high school students showed high frequency and centrality. In addition, it can be seen that studies on the educational effect of geological fieldwork were discussed, either in connection with programs such as STEAM, free-semester program, or indirect geological fieldwork methods such as web, flash panorama, and 3D. This study is meaningful in that it suggests the direction of future research by looking back on the research on geological fieldwork that has been done so far.

• Journal of the Korean Society of Earth Science Education
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• v.13 no.3
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• pp.226-237
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• 2020

The purpose of the research is to analyze research trends related to climate change education by network analysis based on keywords extracted from the research title. For this purpose, 62 papers were selected from Korean Citation Index(KCI) journals published from 2011 to 2020 using such keywords as "climate change" and "climate change education" in the Research Information Sharing Service. The analysis procedure consisted of selection of analysis papers, keyword extraction and purification, and keyword network analysis and visualization. Textom, Ucinet 6.0, and NetDraw were used to analyze the frequency, degree centrality, and betweenness centrality. The results of the research showed that, first, Early 'Energy and Climate Change Education' had the highest frequency of papers examining climate change education. Second, the keywords/phrases that appeared most frequently in research on climate change education were "program" "energy," "analysis," "elementary school," "elementary school," "elementary school students," "development," and "impact." Third, the analysis of the centrality of betweenness centrality showed that the index of 'program', 'primary students' and 'primary schools' were the highest, and the largest group was 'development and effect of teaching and learning programs'. Based on these results, it was concluded that future research on climate change education needs to be examined in further detail and expanded into more specific areas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.3
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• pp.221-228
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• 2013

In areas such as trade area analysis and urban planning, the maturity assessment of central places is important information to be taken into account for rational decision-making. In this study, 3 business areas, typical retail businesses (retail, personal service, food and beverage), are extracted from year 2004 and 2009 phone books, and centrality index and location quotients for 3 business areas are calculated. Using these outputs growth, declination, and maturation of central places are figured out, then comparing and analyzing these results assessment method for central places is proposed. As a result, it is known that the maturity of central place, such as growing, declining, and maturing, can be assessed by comparing the location quotients of 3 retail business areas. In growing central place, location quotient of food and beverage business is much higher than others, and in maturing central place, that of retail business is much higher than others. Assessment results of central places are very useful, as an important index, to determine branch opening or select business area, in trade area analysis.

• The Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.125-140
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• 2015

It is general that the impact of supply chain risk spread out to the whole network along the connected structure. Due to the risk propagation the probability to exposure a certain risk is affected by not only the characteristics of each risk factor but also network structure. It means that the structural connectivity among vertices should be considered while designing supply chain network in order to minimize disruption cost. In this research, the betweenness centrality has been utilized to quantitatively assess the structural vulnerability. The betweenness centrality is interpreted as the index which can express both the probability of risk occurrence and propagation of risk impact. With the structural vulnerability index, it is possible to compare the stability of each alternative supply chain structure and choose the better one.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.25-52
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• 2018

This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.