• Journal of Information Technology Services
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• v.17 no.2
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• pp.179-190
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• 2018

This study is about the influence of authors' centrality on research outcomes in a large-scale collaborative research network. Using the social network analysis method, five types of centralities were derived. Six research outcomes of individual researchers were also derived through bibliographic information of the social science field for the last 10 years. A multivariate regression analysis was conducted to examine the causal relationship between the centrality and research outcome, and the effect of centrality on research outcomes was found to be statistically significant. The result of this study shows that the revised citation and H-index significantly influenced the authors' centrality. This result can imply that the centrality of the researcher can expect a considerable influence of the thesis as well as a certain level of productivity. The meaning of this study is to analyze the effect of centrality on the research outcomes of the large-scale collaborative research network in the past decade, and is carefully to suggest a guideline in order to support new research information services for active researchers and the advancement of collaborative research. This study has its limitation for interpreting the diverse academic fields of the social sciences in a uniform way. In future study, it is necessary to conduct studies using various weighted indices for network centrality in order to measure the influence of research.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.173-184
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• 2014

As social network services has become one of the most successful web-based business, recommendation in social network sites that assist people to choose various products and services is also widely adopted. Collaborative Filtering is one of the most widely adopted recommendation approaches, but recommendation technique that use explicit or implicit social network information from social networks has become proposed in recent research works. In this paper, we reviewed and compared research works about recommendation using social network analysis and collaborative filtering in social network sites. As the results of the analysis, we suggested the trends and implications for future research of recommendation in SNSs. It is expected that graph-based analysis on the semantic social network and systematic comparative analysis on the performances of social filtering and collaborative filtering are required.

• Journal of Smart Tourism
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• v.4 no.1
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• pp.19-29
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• 2024

The objective of this article is to create a model of integrated management from the framework modeling of a digital collaborative network supported by knowledge engineering to make heritage site in the Brazil more effective. It is an exploratory and qualitative research with thematic analysis as technique of data analysis from the collaborative network, digital platform, world heritage, and tourism themes. The snowballing approach was chosen, and the mapping and classification of relevant studies was developed with the use of the spreadsheet tool and the Mendeley® software. The results show that the collaborative network model oriented towards strategic objectives should be supported by a digital platform that provides a technological environment that adds functionalities and digital platform services with the integration of knowledge engineering techniques and tools, enabling the discovery and sharing of knowledge in the collaborative network.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.6
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• pp.618-627
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• 2014

This paper aims to construct and analyze the domestic collaborative research network in industrial engineering. Using co-authorship information contained in the papers published in the two journals of the Korean Institute of Industrial Engineers, the collaborate research network at the institutional level is constructed. The core institutions in the network are identified by means of the centrality indexes of social network analysis. In addition, the five types of roles of the institutions in industry-university-institute cooperation are examined through brokerage analysis: coordinator, consultant, gatekeeper, representative, and liaisons. The findings are expected to be fruitfully utilized in formulation of R&D strategy of relevant organizations and technology policy making for promoting collaborative research in industrial engineering.

• 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.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.11-21
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• 2012

Hybrid network infrastructure has been deployed as the most important technology for the advanced research networking community such as Internet2, SURFnet, etc. However, further research needs to be performed in terms of feasible design and implementation of architecture for inter-domain collaborative network infrastructure, which is essential to end-to-end collaborative research based on high-end applications. In this paper, we suggest a framework for distributed and virtual network operations based on hybrid research networks and efficient cooperation between multi-domain hybrid networks, which aims to provide collaborative network environment for high-end applications. Suggested framework is designed to adopt decentralized model of multi-domain hybrid research network management. A collaborative and distributed virtual model that is characterized by cooperation among hybrid research networks that insist on maintaining their autonomy and control, can also contribute for researchers and other end-users to manage and operate their own virtual networks.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.93-107
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• 2014

Interests on collaborative research and academic relationship among researchers have been increased. Collaborative researchers can maximize productivity, time and cost savings, and reduce the risk of research. An empirical study on the research productivity of co-authors' network and review efficiency of the reviewer network was conducted based on co-author networks and reviewer networks in Korea Society of IT Service. This study aims to find the characteristics of the co-author and reviewer networks, and to analyze research productivity and review efficiency in order to draw some implications. The meaning of interactions among professional groups was analyzed. Research productivity index was calculated using 728 authors' papers submitted to the society. In order to verify the effects of indicators of social network analysis on research productivity and review efficiency, correlation and regression analyses were used. As a result, the indicators of network centrality did not affect the review efficiency, but affect the research productivity.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

• Journal of Information Technology Services
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• v.18 no.4
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• pp.83-95
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• 2019

It is becoming more and more difficult to cope with new knowledge and technology required by society by the efforts of one person or organization according to the development of science and technology. As a method to overcome this, collaborative research is becoming important. This tendency is increasing in the government R&D projects as well, and the 'A' test research institute, which is the subject of this paper, is also increasing a collaborative research. The purpose of this study is to analyze the network characteristics among the participating researchers in the government R&D project conducted by the institution A, and to ascertain how the network characters of the researchers actually affect the financial performance of the team. The results of the analysis show that 'closeness centrality' and 'degree of centrality' contribute positively to the financial performance of the team. On the other hand, 'betweenness centrality' and 'eigenvector centrality' have a negative effect on the financial performance of the team because they are not directly related to financial performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.