• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.57-72
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• 2009

As development of information technologies, customer retention has been an important issue in the competing environment. A lot of researches focus on prediction of the churning customers and seeking their characteristics. However, relationships among customers or products have not been considered in existing researches. In this study, product networks are proposed and analyzed to investigate the differences of network characteristics of products purchased by potential churning customers and those of loyal customers. The product networks are constructed from real product purchase data collected from a Korean department store. We investigated the characteristic differences, such as the degree centrality, degree centralization, and density, of two product networks constructed by potential churning customers and the loyal customers. The results indicate that degree centrality, density and degree centralization of the product network of the loyal customers are higher than those of the potential churning customers. And the promotional products of the department store are resulted to be effective in attracting the loyal customers.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• Perspectives in Nursing Science
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• v.13 no.1
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• pp.29-35
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• 2016

Purpose: This study aimed to explore the underlying structures of students' interaction networks to monitor network changes during the year, to verify the relationship with self-directed learning, and to identify the effect of problem-based learning on interaction and self-directed learning. Methods: A longitudinal study was designed which included 3 parts (A=25, B=27, C=26) with a total of 78 second-year nursing students from 2013 to 2014. Interaction indicators used group network centralization and density, and individual in-degree centrality. Results: Group network centralization showed mean reversion patterns, however, centralization and density showed a slight increase from 2013 to 2014 (Centralization of A part from 52.78 to 36.96, B part from 20.56 to 32.20, C part from 34.40 to 37.24; Density of A part from 0.122 to 0.123, B part from 0.111 to 0.121, C part from 0.109 to 0.121). The individual in-degree centrality is significantly correlated with self-directed learning and the correlation coefficient increased during the year (r=.274 in 2013, r=.356 in 2014, p<.001). Conclusion: Students share information more interactively during the year and the more they share the higher the scores of self-directed learning.

• 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.

• Journal of Korean Society of Rural Planning
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• v.23 no.2
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• pp.9-17
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• 2017

Rural development policy has changed from hardware based development to community revitalization. The purpose of this study is to analyze social network of collaborative activity among rural organizations as fundamental of community. The material used in this study is a record of collaborative activites in the community newsletter of Hong-Dong area. 161 of collaborative activities (links) and 75 of organizations (nodes) are investigated in network. 6 collaborative activity type ('Education', 'Socializing', 'Meeting', 'Culture', 'Event' and 'Labor') is classified. 'Socializing' is inclusive of approximately half of whole network (50.67%). Closeness centraization, degree centralization and betweenness centralization are measured on top in 'Education', 'Meeting' and 'Event' type. Scatter plot analysis using degree and betweenness centrality index, 'Maeul Revitalization Center', 'Balmak Library', 'Woori-Maeul Medical Co-op', 'Support Center for Female Farmers', 'Hongdong Middle School' and 'Mundang Sustainable Agriculture Education Center' are resulted as the core organization in network. Geographical distribution of collaborative activity is not only concentated in Hong-Dong Myeon but also networked with adjacent administrative district. This study finds its purpose in the detailed analysis of network characteristics of collaborative activity within Hong-Dong area which is representative developed rural community in Korea.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.2
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• pp.157-171
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• 2014

A organization of social enterprises is to achieve various goals such as private interests, the public nature, and social policy. For fulfilling these goals, we have to understand the various success factors. These success factors were shared among peoples. This study explored a position of structure of social network formed by success factors of Social Enterprises with importance. A position within social network defined a number of link connected other nodes. A position is closely associated with to individual's behaviors, opinions and thinking. We used social network analysis with two mode method for explaining feathers of structure of social network formed by success factors shared among peoples. We choose degree centrality for determining a position within social network. Centrality is a key measure in social network analysis. Results is that shared success factors are operation capital(15.15%) totally, and by Buying experience of products of Social Enterprises, Business Compliance(14.39%) and planning(12.88%), and by usage time of smart devices, Business Support(17.05%) and planning(16.10%). and the dominant success factor was not explored.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1414-1423
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• 2014

This paper aims to analyze destination composition patterns included in package products and to identify the characteristics of the tourism destinations through social network analysis using data collected from tour packages distributed by major Korean travel agencies, targeting Singapore as a key destination. Such tour package data were transcribed into data matrix using Pajek, UCINET, and Cytoscape for analysis. The density and centralization scores derived from the analysis indicated clearly that each destination was almost connected to each other. The centrality scores indicated that the destinations with higher degree of centrality also has higher betweenness centrality within the network. Based on centrality scores, POI(point of interests) of tourism destinations showed concentration in southern area of the target region. Finally, through component analysis, subgroups of tourism destination networks are isolated. Practical implications were also presented for industry practitioners.

• Korean Parent-Child Health Journal
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• v.19 no.1
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• pp.25-32
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• 2016

Purpose: The purpose of this study was to use the social network analysis to verify the relationship between the interpersonal disposition and interactive characteristics of nursing college students. Five classes, a total of 117 nursing students were included in the study. Methods: The study interaction network centralization, density and in-degree & out-degree centrality were used as indicators for measuring students' study interaction. Also, the students' interpersonal disposition was measured using verified tool. Results: The results indicated that the ordinary type which gave and took the information generally showed the highest proportions in the study interaction network. Also, the empathic-acceptable tendency of social relation domain showed the highest proportions in the interpersonal disposition. Moreover, the students who play the isolate and the carrier role in the study interaction network had no relation with the expression tendency, and the transmitter role play students had no relation with the role tendency. Conclusion: Based on the results, the tutors should consider the students' interpersonal relationship disposition when implementing the instructional strategy in PBL curriculum.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.291-315
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• 2019

The aim of the present study was to compare the research trends on disinformation between Korean and abroad. To achieve this objective, a total of 283 author-assigned English keywords in 104 Korean papers and 3,551 author-assigned English keywords in 861 abroad papers were collected from the whole research fields and the publication periods. The collected data were analyzed using NetMiner V.4 to discover their 'degree centrality' and 'betweenness centrality'of the keyword network. The result are as follows. First, the major research topics of disinformation in Korea were drawn such as 'Freedom of Expression', 'Fact Check', 'Regulation', 'Media Literacy', and 'Information Literacy' in order; whereas, in abroad were shown like 'Social Media', 'Post Truth', 'Propaganda', 'Information Literacy', and 'Journalism' in order. Second, in terms of the influence of research topics related to disinformation, in Korea were identified such as 'Fact Check', 'Freedom of Expression', and 'Hoax' in order; whereas, in abroad were shown such as 'Social Media' and 'Detection' in order. Finally, in an aspect of intervention of research topics related to disinformation, in Korea were 'Fact Check', 'Polarization', 'Freedom of Expression', and 'Commercial'; whereas, in abroad were 'Social Media', 'Detection', and 'Machine Learning' in order.

• Radiation Oncology Journal
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• v.28 no.3
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• pp.184-191
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• 2010

Purpose: To evaluate the structural characteristics of a scientific network of radiation oncology society. Materials and Methods: A total of 1,512 articles published from 1986 to April 2010 with the terms 'radiation oncology' or 'therapeutic radiology' were obtained in the KoreaMed database. The co-authors were analyzed according to their affiliation, and their relationship was used to build a matrix. With the matrix, centralization indices and the Key Player index were analyzed. We used UCINET 6.0 for the network analysis, Netdraw for determining a sociogram and Key Player 1.44 for the key player analysis. Results: The centralization of the radiation oncology field decreased from 8.29% for the period from 1986~1990 to 1.84% from 2006~2010. However, when the Korean Journal of Medical Physics was excluded, centralization increased from 2.32% for the period from 2001~2005 to 3.80% from 2006~2010. This suggested that the communication in the clinical research field of radiation oncology is decreasing. In a node centralization analysis, Seoul National University was found to be the highest at 7.9%. Seoul National University showed the highest indices in the Outdegree (6.50%) and Indegree (8.54%), in addition to Betweenness (14.94%) and Eigenvector (135.234%). The Key Player analysis indicated that Inha University had the highest index at 0.491, but when the Korean Journal of Medical Physics was excluded, Yonsei University had the highest Key Player index at 0.584. Conclusion: The degree centrality in the network of radiation oncology decreased in the most recent period as more institutions are participating in network. However, the Betweenness centrality is still increasing, suggesting that the communications among research groups (clique) in radiation oncology is warranted.