• Convergence Security Journal
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• v.21 no.2
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• pp.47-56
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• 2021

Network topology visualization has been studied a lot since the past and developed with many tools. The network topology has strength in understanding the overall structure of a network physically and is useful for understanding data flow between nodes logically. Although there are existing tools, not many can be utilized efficiently while using the general network node data structure and express the topology similar to the actual network structure. In this paper, we propose an efficient method to visualize topology using only connection information of network nodes. The method finds the central node by using the centrality, the influence of nodes in the network, and visualizes the topology by dynamically segmenting all nodes and placing network nodes in 3D space using the weight of the child node. It is a straightforward method, yet it effectively visualizes in the form of an actual network structure.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.47-54
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• 2022

We analyze and compare the structure of the networks of Netflix, Disney+, and OCN, which are forerunners in OTT market, on Twitter. This study employs NodeXL pro as a visualization software package for social network analysis. As a result of the comparison with values of Vertices, Connected Components, Average Geodesic Distance, Average Betweenness Centrality, and Average Closeness Centrality. Netflix has comparative advantages at Vertices, Connected Components, and Average Closeness Centrality, OCN at Average Geodesic Distance, and Disney+ at Average Betweenness Centrality. Netflix has a more appropriate social network for influencer marketing than Disney+ and OCN. Based on the analysis results, the purpose of this study is to explain the structural differences in the social networks of Netflix, Disney+, and OCN in terms of influencer marketing.

• Convergence Security Journal
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• v.22 no.4
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• pp.169-178
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• 2022

In general, organizations operating multi-domain networks find it difficult to represent and manage multiple domain net works on a single screen space. Instead, most of them are managed with multiple screens visualizing network topology by domain or partitioning one screen area into multiple domains. We propose an efficient method to visualize the topology using only minimal connection information between domain-agnostic nodes in this work. This method visualizes the topology by utilizing centrality indices representing the influence of nodes in the network. Furthermore, the method dynamically segments the entire node's display area using virtual Root nodes to auto-separate domains and weights of child nodes and placing nodes in 3D space. Thus, although it is a straightforward method, the multi-domain network topology can be visualized with only minimal connection information between nodes.

• Journal of Korean Academy of Nursing
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• v.46 no.1
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• pp.50-58
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• 2016

Purpose: The purpose of this study was to identify the knowledge structure of cancer survivors. Methods: For data, 1099 articles were collected, with 365 keywords as a Noun phrase extracted from the articles and standardized for analyzing. Co-occurrence matrix were generated via a cosine similarity measure, and then the network analysis and visualization using PFNet and NodeXL were applied to visualize intellectual interchanges among keywords. Results: According to the result of the content analysis and the cluster analysis of author keywords from cancer survivors articles, keywords such as 'quality of life', 'breast neoplasms', 'cancer survivors', 'neoplasms', 'exercise' had a high degree centrality. The 9 most important research topics concerning cancer survivors were 'cancer-related symptoms and nursing', 'cancer treatment-related issues', 'late effects', 'psychosocial issues', 'healthy living managements', 'social supports', 'palliative cares', 'research methodology', and 'research participants'. Conclusion: Through this study, the knowledge structure of cancer survivors was identified. The 9 topics identified in this study can provide useful research direction for the development of nursing in cancer survivor research areas. The Network analysis used in this study will be useful for identifying the knowledge structure and identifying general views and current cancer survivor research trends.

• Health Communication
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• v.12 no.1
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• pp.51-72
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• 2017

Purpose: Adolescents use emoticons to express their emotions in an online environment. Hence, medical experts can understand the emotions of adolescents by emoticons. The goal of this study was to investigate the relationship between various emotions and emoticons among the Korean adolescents. Methods: The questionnaire survey was conducted between September 1 and 30, 2014, involving 3,272 students in elementary schools, middle schools, and high schools affiliated in the Department of Education of the metropolitan city of Busan. A total of 1,717 students responded to the survey. The participants consisted of 806 males (46.9%), and 911 females (53.1%). Among these, there were 557 elementary school students (32.4%), 617 middle school students (35.9%), and 543 high school students (31.6%). A social networking analysis was conducted using NodeXL. Results: The frequency of emoticon use among adolescents runs in the order of joy, sadness, fear, surprise, anger, disgust, and then depression. Elementary school females mainly use emoticons to express joy; middle school females use emoticons to express sadness, surprise, anger, disgust, and depression; and high school females use emoticons to express fear. Age- and gender-specific emoticon networks were visualized by using the Haren-Korel fast multiscale algorithm. Commonly used emoticons by age and gender were expressed in the networks. Results of age- and gender-specific emoticon networks visualization show similar results of centrality of seven emoticons. Conclusion: In the digital communication environment, emoticons could be used to catch the emotions of adolescents in Korea.