• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.77-91
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• 2019

This paper explores the hyperlink structures of the official websites for the following terrestrial TV networks: ABC(US), BBC(UK), NHK(Japan), and KBS(Korea). These websites were selected and visualized to analyze the hyperlink structure and examine the connection relations among the TV networks. A total of 4378 data was collected through the Voson site and were analyzed with NodeXL. Results shows that NHK's network demonstrates a good network structure at a quite high level, holding more related websites than BBC. We discovered that ABC TV network has the largest effect with the largest number of out-links. Surprisingly, structures of BBC and NHK were quite similar, overcoming geographical and cultural differences. Thus, both TV networks were seen to be progressive and open. On the contrary, ABC and KBS were considered to be relatively conservative. A comprehensive review of the "category points" combination chart revealed that NHK's official website has the widest variety of hyperlinks. The shortest distance of a hyperlink between a website type and a TV network meant that the TV network has a larger number of links to those website types than other TV networks do. The result may provide Internet users to efficiently select TV network web pages according to the types of information they want to find out.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1589-1595
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• 2018

Similarity index measures the topological proximity of node pairs in a complex network. Numerous similarity indices have been defined and investigated, but the dependency of structure on the performance of similarity indices has not been sufficiently investigated. In this study, we investigated the relationship between the performance of similarity indices and structural properties of a network by employing a two-state random network. A node in a two-state network has binary types that are initially given, and a connection probability is determined from the state of the node pair. The performances of similarity indices are affected by the number of links and the ratio of intra-connections to inter-connections. Similarity indices have different characteristics depending on their type. Local indices perform well in small-size networks and do not depend on whether the structure is intra-dominant or inter-dominant. In contrast, global indices perform better in large-size networks, and some such indices do not perform well in an inter-dominant structure. We also found that link prediction performance and the performance of similarity are correlated in both model networks and empirical networks. This relationship implies that link prediction performance can be used as an approximation for the performance of the similarity index when information about node type is unavailable. This relationship may help to find the appropriate index for given networks.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.12-24
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• 2022

This study attempted to investigate changes in collaboration structure for each stage of national Research and Development (R&D) in the artificial intelligence (AI) field through analysis of a co-author network for papers written under national R&D projects. For this, author information was extracted from national R&D outcomes in AI from 2014 to 2019. For such R&D outcomes, NTIS (National Science & Technology Information Service) information from the KISTI (Korea Institute of Science and Technology Information) was utilized. In research collaboration in AI, power function structure, in which research efforts are led by some influential researchers, is found. In other words, less than 30 percent is linked to the largest cluster, and a segmented network pattern in which small groups are primarily developed is observed. This means a large research group with high connectivity and a small group are connected with each other, and a sporadic link is found. However, the largest cluster grew larger and denser over time, which means that as research became more intensified, new researchers joined a mainstream network, expanding a scope of collaboration. Such research intensification has expanded the scale of a collaborative researcher group and increased the number of large studies. Instead of maintaining conventional collaborative relationships, in addition, the number of new researchers has risen, forming new relationships over time.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.635-660
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• 2024

A growing body of research explores how users of online communities navigate through large-scale platforms to find the information they seek. This study builds on the theories of homophily, structural embeddedness, and social exchange to investigate how interest homophily and existing communication structures serve as mechanisms driving information searches and the subsequent formation of communication networks in these communities. Specifically, we analyze comment-on-post tie formation using network data from "Today's House," the largest online user community specializing in interior design in Korea. Employing the LR-QAP method, a permutation-based hypothesis testing algorithm for social network data, our research identifies that network tie formation is driven by both homophilous information searches based on instrumental and hedonic interests, as well as by structurally induced searches such as preferential attachment, reciprocity, and transitivity. In addition, we investigate the contingent effects of communication structure on homophilous tie formation. Our findings suggest that while network-wide structural characteristics enhance homophilous tie formation based on instrumental interests, local network processes leverage homophily based on hedonic interests. We conclude by discussing the theoretical implications of the differential influence of participation motivations on information search patterns and the practical implications for the design of online communities.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.143-163
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• 2018

The business models has a great impact on the successful management of enterprises. Business environment has been shifting from industrial economy to knowledge-based economy. Enterprises go through numerous trials for successful management in the changing environment. Along with trial tests, research areas have been growing simultaneously. Although many researches have been conducted with regard to business models, it is very insufficient to systematically analyze the knowledge flow of research. Accordingly, successive researchers who want to study the business model may find it difficult to establish the orientation of future application research based on understanding the process of changing the knowledge structure that have accumulated so far. This study is intended to determine the current state of the business model research and to understand the process of knowledge structure changes in keywords that appear in 2,667 business model articles in the SCOPUS database. Identifying the knowledge structure has been completed through social network analysis, a methodology based on the 'relationship', and the changes in the knowledge structure were identified by classifying them into four different periods. The analysis showed that, first, the number of business model co-author increases over time with the need for academic diversity. Second, the 'innovation' keyword has the biggest center in the network, and over time, the lower-rank keyword which was in the former period has emerged as the top-rank keyword. Third, the cohesiveness group decreased from 12 before 2000 to 5 in 2015 and also the modularity decreased as well. Finally, examining characteristics of study area through a cognitive map showed that the relationships between domains increased gradually over time. The study has provided a systematic basis for understanding the current state of the business model research and the process of changing knowledge structure. In addition, considering that no research has ever systematically analyzed the knowledge structure accumulated by individual researches, it is considered as a significant study.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.447-455
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• 2019

Since the forest type map in Korea has been mostly constructed every five years, the forest information from the map lacks up-to-date information. Forest research has been carried out by aerial photogrammetry and field surveys, and hence it took a lot of times and money. The vertical structure of forests is an important factor in evaluating forest diversity and environment. The vertical structure is essential information, but the observation of the vertical structure is not easy because the vertical structure indicates the internal structure of forests. In this study, the index map and texture map produced from KOMPSAT-3/3A/5 satellite images and the canopy information generated by the difference between DSM (Digital Surface Model) and DTM (Digital Terrain Model) were classified using the artificial neural network. The vertical structure of forests of single and multi-layer forests was classified to identify 81.59% of the final classification result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5927-5942
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• 2017

Many real networks exhibit overlapping community structures. Recent studies have been performed that analyze the impact of overlapping community structure on information propagation, but few of them concerned with individual behaviors. From this point of view, we propose a Markov process model to evaluate the performance of information propagation in social networks with overlapping community structures. In addition, many individual social behaviors are combined in the model. For example, individuals may exhibit selfish behaviors, such as individual and social selfishness, and people may discard the information after they have used it. The accuracy of the model is verified by simulation. Furthermore, the numerical results show that both overlapping community structure of the network and individual behaviors have a significant impact on the outbreak size and propagation speed of the information. Additionally, the overlapping community structure of the social network can reduce the impact of selfishness on information propagation.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.739-742
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• 1997

This paper proposes the structure and parameter optimization technique of fuzzy neural networks using genetic algorithm. Fuzzy neural network has advantages of both the fuzzy inference system and neural network. The determination of the optimal parameters and structure of the fuzzy neural networks, however, requires special efforts. To solve these problems, we propose a new learning method for optimization of fuzzy neural networks using genetic algorithm. It can optimize the structure and parameters of the entire fuzzy neural network globally. Numerical example is provided to show the advantages of the proposed method.

• Journal of the Ergonomics Society of Korea
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• v.20 no.2
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• pp.29-46
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• 2001

This study is about the modeling of comfort feeling structure in indoor environments. To represent the degree of practical comfort feeling level in an environment, we measured elements of human sense and resultant elements of comfort feeling such as coziness, refreshment, and freshness with physical values(temperature, illumination, noise. etc.). The relationships of elements of human sense and elements of comfort feeling were formulated as a fuzzy model. And a hybrid-neural network with three layers were designed where obtained from fuzzy membership function values of the elements of human sense were used as inputs, and given as fuzzy membership function values of resultant elements of comfort feeling were used as outputs. Both kinds of fuzzy membership function values were obtained from physical values. The network was trained by measured data set. The proposed hybrid-neural network were tested and proposed a more realistic model of comfort feeling structure in indoor environments.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.173-180
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• 2018

In this paper, we analyzed key keywords and research themes in the field of defense research using keyword network analysis and tried to grasp the whole knowledge structure. To do this, we extracted data from 2,165 research data from defense related research institutes from 2010 to 2017 and applied the Pareto rule to the number of abstracts of words and the number of links between words, We extracted a total of 2,303 words based on the criterion and extracted 204 final key words through component analysis. By analyzing the centrality and cohesiveness through these key words, we confirmed the concept of core research in the defense field and derived a total of 7 large groups and 16 small groups of each group in the knowledge structure of the defense area.