• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.685-697
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• 2019

The study aimed to analyze the structure of China's passenger railway network in the years of 2008, 2013, and 2018, respectively. At the same time, it tried to investigate its derivative impact on the patterns of Chinese urban network. The analytical tool was based on the NetMiner4.0. In order to measure network characteristics of China's passenger railway network, it primarily focused on the degree centrality, betweenness centrality, and closeness centrality. First of all, the higher degree centralities, with a few exceptions, were observed in BeiJing, ShangHai, GuangZhou, WuHan, XiAn, ChengDu, HaErBin, and ShenYang over a decade. In contrast, the higher betweenness centralities were recorded in cities of higher development potential including WuLuMuQi, GuiYang, ShenYang, and KunMing. The closeness centrality analyses confirmed the fact that most metropoles like BeiJing, ShangHai, and GuangZhou kept the highest train accessibility during the same research period. At the same time, the opening up of a new stretch of high speed railway network has consecutively strengthened connectivity between BeiJing and TianJin. Owing to unprecedented development of railway traffic and its extensive operations, this study believes that Chinese major cities, without interruption, would pursue a series of urban policy alternatives geared towards railway stations-oriented networking and competitively try to extend their network ranges.

• Spatial Information Research
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• v.23 no.3
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• pp.35-44
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• 2015

Recently, the government aims to discover and provide services to citizens on the development strategy for activating the U-City. So, this study aims to offer a service discovery direction by analyzing the service issues and trends. The target is newspaper article about U-City Service from 2009 to 2014. Prepared 723 newspaper article for analysis. Next step is frequency analysis of keyword and used that result for Network analysis and measure of information. Network analysis can show result through "Degree Centrality", "Betweenness Centrality" and "Closeness Centrality". As a result, "Information", "IT", "Environment", "Technology", "Center" is higher than another. These 5 keywords are important factors for driving the U-City the past six years. Information measurement results, Already U-City were put an emphasis on building the infrastructure and able to identify a trend that provided the center of the public service. Those Service field are "Tour(2009)", "Crime prevention and Disaster Prevention(2010)", "Facility Management(2011)", "administration(2012)" and "Facility Management(2013, 2014)". Result of this study found implications what on citizen participation. So, services field on the existing infrastructure should be discovered and provided. Finally, this study can expected to be a reference in the local government planning for U-City.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• 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.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.159-182
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• 2009

We construct product networks from the retail transaction dataset of an off-line department store. In the product networks, nodes are products, and an edge connecting two products represents the existence of co-purchases by a customer. We measure the quantities frequently used for characterizing network structures, such as the degree centrality, the closeness centrality, the betweenness centrality and the centralization. Using the quantities, gender, age, seasonal, and regional differences of the product networks were analyzed and network characteristics of each product category containing each product node were derived. Lastly, we analyze the correlations among the three centrality quantities and draw a marketing strategy for the cross-selling.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.347-349
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• 2009

Air quality of indoor space environment is affected by various pollutants like as particles and chemical stuffs. The indoor air pollution affects directly the human respiration organs to cause consequently unpleasant mental status. The $CO_2$ concentration level is one of the harmful components of air pollutants. Major factor to increase the $CO_2$ concentration level is the people's breath amount in indoor. The car exhaust gas diffused from the around road also has strong affect on $CO_2$ concentration. There are some other reasons to affect the $CO_2$ concentration change, such as, real-time change of the population movement, closeness to the indoor air flow inlet window and changes in road car traffic amount. A remote monitoring system to measure environmental indoor air pollution concerning on the $CO_2$ concentration was studied and installed realized set-up model. Zigbee network configuration was applied for this system and the $CO_2$ concentration data were collected through USN network. A software program was developed to assure systematic analysis and to display real-time data on web pages. For the experimental test various condition was set up, like as, window opening, stopping air condition operation and adjusting fan heater work, etc. The analysis result showed the relation of various environmental conditions to $CO_2$ concentration changes. The causes to increase $CO_2$ concentration were experimentally defined as windows closing, the stopping air condition system, fan heater operation. To keep the $CO_2$ concentration under the legally required ppm level in public access indoor space, the developed remote measurement system will be usefully applied.

• Korea Real Estate Review
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• v.27 no.3
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• pp.51-70
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• 2017

This study aimed to measure street centralities with the street width, and to analyze their effects on the residential and non-residential land prices in Seoul, South Korea. Most of the studies on urban economics and policy focusing on the urban spatial structure have evolved in terms of their perspective from monocentric to polycentric models. Recently, their themes shifted to measuring street centralities and capturing their effects on urban phenomena. To expand the existing studies and discussion, this study analyzed the street centralities with the street width, and how they changed the land prices. Multilevel regression models generated a few key findings relevant to the relationship between street centralities and land prices. While a higher detour volume and closeness to wider streets commanded premium residential land prices, higher visibility and detour volume to wider streets were associated with higher non-residential land prices. These findings suggest a robust connection between street configuration and near-land prices. Thus, the results of this study suggest a few insightful policy implications for urban planners, urban designers, real estate developers, and appraisers.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.11
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• pp.1691-1699
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• 2018

Objective: In Korea, there are three main cattle breeds, which are distinguished by coat color: Brown Hanwoo (BH), Brindle Hanwoo (BRH), and Jeju Black (JB). In this study, we sought to compare the genetic diversity and divergence among there Korean cattle breeds using a BovineHD chip genotyping array. Methods: Sample data were collected from 168 cattle in three populations of BH (48 cattle), BRH (96 cattle), and JB (24 cattle). The single-nucleotide polymorphism (SNP) genotyping was performed using the Illumina BovineHD SNP 777K Bead chip. Results: Heterozygosity, used as a measure of within-breed genetic diversity, was higher in BH (0.293) and BRH (0.296) than in JB (0.266). Linkage disequilibrium decay was more rapid in BH and BRH than in JB, reaching an average $r^2$ value of 0.2 before 26 kb in BH and BRH, whereas the corresponding value was reached before 32 kb in JB. Intra-population, interpopulation, and Fst analyses were used to identify candidate signatures of positive selection in the genome of a domestic Korean cattle population and 48, 11, and 11 loci were detected in the genomic region of the BRH breed, respectively. A Neighbor-Joining phylogenetic tree showed two main groups: a group comprising BH and BRH on one side and a group containing JB on the other. The runs of homozygosity analysis between Korean breeds indicated that the BRH and JB breeds have high inbreeding within breeds compared with BH. An analysis of differentiation based on a high-density SNP chip showed differences between Korean cattle breeds and the closeness of breeds corresponding to the geographic regions where they are evolving. Conclusion: Our results indicate that although the Korean cattle breeds have common features, they also show reliable breed diversity.