• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.420-428
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• 2010

The advancement of the Science for complex systems enables the analysis of many social networks. We constructed and analyzed a Korean movie star network as one of social networks, based on the 100 Korean movie selection for a main data source. Until now, the research trend has been the structural analysis of network, focused on link numbers, such as degree, betweenness and clustering coefficient. But it is time that the research is not limited by the structural analysis of networks only. Rather, the research goal should be aimed to an information analysis, performed by identifying and analyzing central modules that are regarded as the core of complex networks, using k-core analysis method. In this research, we constructed a network of movie stars who have appeared in 100 Korean movie selection, provided by Korean movie database, also we analyzed its core modules with and without weights, and the trend of seasonal expansion of the network. We expect our findings can be used as the basic data applicable to a model for understanding of the expansion and evolution of networks.

• The Journal of Information Systems
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• v.33 no.1
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• pp.45-68
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• 2024

Purpose This paper examines the role of local market-based communities in driving regional revitalization, using detailed analysis of online networks. We aim to dissect a local community's communication network, highlighting members with high engagement levels and exploring their characteristics. Our goal is to identify the conditions that allow local community networks to grow independently and to demonstrate how the activation of these networks contributes to regional revitalization. Design/methodology/approach We employ a mixed-methods approach, combining social network analysis with statistical techniques to investigate the structure of online communication networks. Specifically, we use ANOVA to determine the statistical significance of our findings, ensuring their reliability. To complement our quantitative data, we include qualitative insights from interviews, adding depth and context to our analysis. Findings Our results show that individuals with high centrality in the online network are crucial for maintaining active local communities. We find that leveraging local resources to create a supportive and adaptable environment is essential for the communities' sustainability and expansion. Importantly, our research draws a direct connection between the vitality of local community networks and the broader process of regional revitalization. We argue that energizing local communities is an effective way to address the risk of regional decline. By integrating quantitative analysis with qualitative feedback, this study contributes to the understanding of local market-based communities as key drivers of regional development. It emphasizes the importance of building vibrant, resourceful community networks to revitalize areas experiencing socio-economic challenges.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.205-213
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• 2021

The properties of a scale-free network are little known; its node degree following a power-law distribution is among its few known properties. By selecting real-field scale-free networks from a network dataset and comparing them to other networks, such as random and non-scale-free networks, the topology characteristics of scale-free networks are identified. The assortative coefficient is identified as a key metric of a scale-free network. It is also identified that most scale-free networks have negative assortative coefficients. Traditional generation models of scale-free networks are evaluated based on the identified topology characteristics. Most representative models, such as BA and Holme&Kim, are not effective in generating real-field scale-free networks. A link-rewiring method is suggested that can control the assortative coefficient while preserving the node degree sequence. Our analysis reveals that it is possible to effectively reproduce the assortative coefficients of real-field scale-free networks through link-rewiring.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• Journal of the Chungcheong Mathematical Society
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• v.35 no.1
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• pp.39-52
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• 2022

We introduce Clifford-valued neural networks with leakage delays. Furthermore, we study the uniqueness and existence of Clifford-valued Hopfield artificial neural networks having the Stepanov weighted pseudo almost periodic forcing terms on leakage delay terms. However the noncommutativity of the Clifford numbers' multiplication made our investigation diffcult, so our results are obtained by decomposing Clifford-valued neural networks into real-valued neural networks. Our analysis is based on the differential inequality techniques and the Banach contraction mapping principle.

• Journal of Communications and Networks
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• v.10 no.3
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• pp.268-286
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• 2008

In this work, we review the routing models that use game theoretical methodologies. A very common assumption in the analysis and development of networking algorithms is the full cooperation of the participating nodes. Most of the analytical tools are based on this assumption. However, the reality may differ considerably. The existence of multiple domains belonging to different authorities or even the selfishness of the nodes themselves could result in a performance that significantly deviates from the expected one. Even though it is known to be extensively used in the fields of economics and biology, game theory has attracted the interest of researchers in the field of communication networking as well. Nowadays, game theory is used for the analysis and modeling of protocols in several layers, routing included. This review aims at providing an elucidation of the terminology and principles behind game theory and the most popular and recent routing models. The examined networks are both the traditional networks where latency is of paramount importance and the emerging ad hoc and sensor networks, where energy is the main concern.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.95-105
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• 2000

The Purpose of this study is to present a new Trip Generation Model using Backpropagation Neural Networks. For this purpose, it is compared the performance between existing linear/nonlinear Regression models and a new TriP Generation model using Neural Networks. The study was performed according to the below. First, it is analyzed the limits of conventional Regression models, next Proved the superiority of Neural Networks model in theoretical and empirical aspects, and lastly Presented a new approach of Trip Generation methodology. The results show that Backpropagation Neural Networks model is predominant in estimation and Prediction comparable to Regression analysis. Such results mean the possibility of Neural Networks\` application in Trip Generation modeling. Specially under the circumstances of the chancing transportation situations and unstable transportation on vironments, its application in transportation fields will be extended.

• Journal of Communications and Networks
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• v.13 no.1
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• pp.43-49
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• 2011

In this paper, a new medium access control (MAC) protocol entitled three-phase multiple access with continual contention resolution (TPMA-CCR) is proposed for wireless multi-hop ad hoc networks. This work is motivated by the previously known three-phase multiple access (TPMA) scheme of Hou and Tsai [2] which is the suitable MAC protocol for clustering multi-hop ad hoc networks owing to its beneficial attributes such as easy collision detectible, anonymous acknowledgment (ACK), and simple signaling format for the broadcast-natured networks. The new TPMA-CCR is designed to let all contending nodes participate in contentions for a medium access more aggressively than the original TPMA and with continual resolving procedures as well. Through the systematical performance analysis of the suggested protocol, it is also shown that the maximum throughput of the new protocol is not only superior to the original TPMA, but also improves on the conventional slotted carrier sense multiple access (CSMA) under certain circumstances. Thus, in terms of performance, TPMA-CCR can provide an attractive alternative to other contention-based MAC protocols for multi-hop ad hoc networks.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.115-124
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• 1994

In this paper, we have developed a performance analysis tool for communication networks called COMNAS. COMNAS analyses the performance of wide area networks such as Korea Educational Network and Korea Research Environment Open Network which include local area networks such as Ethernet and Token Ring. COMNAS consists of model constructor, simulation implementor, output analyzer and user interface. Attributes of communication networks for modeling either have default values or are entered by user as object units, and implementation of simulation is automatically proceeded by user interface. Ouput results obtained by COMNAS are the status of node, link and entire network such as mean message transmission delay, throughput, utilization, and so on, and they can be selectively obtained upon the request of the user.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.