• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.592-593
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• 2012

Most of the previous works of visualization focus on representing user's relationship on social network by a complex multi dimension graph. However, this method is difficult to identify the important of relationship to focus on personal user intuitively. Besides, the content of written information by user to reflect the interrelation between users is insufficient, since most of visualization methods represent the user relationship using an amount of message and the reference of user's message. In order to resolve above problem, this paper proposes a new visualization method using the user's correlation and user relationship of network node.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.3
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• pp.75-86
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• 2014

An urban ecosystem is a complex system that includes social, economic and ecosystems. Therefore, it is important to consider its environmental capacity while developing a city plan. Most of the plans, however, consider only the social aspects, which fragments the green spaces and disturbs the movement of species. Sungnam has approximately 100 parks with unexecuted development plans and with great potential to contribute towards urban ecosystem enhancement. Therefore, this study applied network analysis to prioritize the development of city parks and contribute towards improving the green network, with Parus spp. as the target species. To compensate for the drawbacks of binary and possibility-based network analysis, this study included two indices, namely $BC^{PC}_K$, $BC^{IIC}_K$, $dPCconnector_k$ and $dIICconnector_k$. These indices make it possible to find patches that could play an important role in green network enhancement. The urban park with greater value gets a higher priority to be transformed into a park. Thus, our methodology could prove to be very useful in prioritizing the undeveloped parks, thereby supporting decision-making.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4372-4394
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• 2020

The distribution of popular videos incurs a large amount of traffic at the base stations (BS) of networks. Device-to-multi-device (D2MD) communication has emerged an efficient radio access technology for offloading BS traffic in recent years. However, traditional studies have focused on synchronous user requests whereas asynchronous user requests are more common. Hence, offloading BS traffic in case of asynchronous user requests while considering their time-varying characteristics and the quality of experience (QoE) of video request users (VRUs) is a pressing problem. This paper uses social stability (SS) and video loading duration (VLD)-tolerant property to group VRUs and seed users (SUs) to offload BS traffic. We define the average amount of data transmission (AADT) to measure the network's capacity for offloading BS traffic. Based on this, we formulate a time-varying bipartite graph matching optimization problem. We decouple the problem into two subproblems which can be solved separately in terms of time and space. Then, we propose the socially aware D2MD user selection (SA-D2MD-S) algorithm based on finite horizon optimal stopping theory, and propose the SA-D2MD user matching (SA-D2MD-M) algorithm to solve the two subproblems. The results of simulations show that our algorithms outperform prevalent algorithms.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.455-468
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• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.77-80
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• 2011

그래프는 전산학의 주요 주제 중 하나이며 World Wide Web과 Social Network의 중요성이 커지면서 더욱 주목을 받고 있다. 그래프와 관련하여 그래프 데이터베이스에 대한 질의 모델에 관한 연구도 중요하게 다투어져 왔다. 하지만 이들 연구는 패턴 매칭을 통한 질의를 주로 다루었다. 하지만 그래프 데이터를 추천이나 검색 등의 응용하기 위해서는 PageRank 등 그래프 내의 연결 구조를 양으로 분석해내는 작업이 요구된다. 또한 SimRank 및 Random Walk with Restart 등 다양한 양적 분석 측도가 제안되고 있다. 이에 따라 본 연구에서는 Random Walk를 기반으로 하는 그래프에 대한 유연한 양적 분석을 지원하는 질의 언어를 제시한다. 또한 기존의 양적 분석 측도들이 본 질의 모델을 통하여 어떻게 표현되는지를 통하여 본 질의 모델의 유용성 및 확장성을 보인다.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.563-573
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• 2012

Various methods of analysis have been proposed to understand the gene-disease relation and gene-gene interaction effect for a disease through comparison of genotype in case-control study. In this study, we proposed the method to detect a genetic association and gene-gene interaction through the use of a network graph and centrality measures that are used in social network analysis. The applicability of the proposed method was studied through an analysis of real genetic data.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.22-24
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• 2012

소셜 애플리케이션은 GPS 센서가 내장된 스마트폰의 보급과 더불어 위치 기반 서비스와 융합된 형태로 발전하고 있다. 기존의 위치기반 서비스는 물류관제, 교통관제, 주문배달 등의 실시간 서비스 제공에 초점을 맞추었기 때문에, 소셜 애플리케이션에서 제공하는 취미, 선호도 기반의 추천 서비스 등의 개인화 서비스 제공에 적합하지 않다. 본 연구에서는 개인화된 소셜 애플리케이션에 적용 가능한 시공간 데이터의 그래프 모델링 기법을 제안한다. 실험을 통해 제안하는 시공간 데이터 모델링 기법의 유용성을 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1270-1271
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• 2011

소셜 네트워크 서비스는 가트너에서 2011년에 이어 2012년에도 각광받을 기술의 하나로 선정된 만큼 미래 인터넷의 핵심 키워드 중 하나로도 뽑히며, 엔터테인먼트, 검색, 방송, 커머스 등의 여러 가지 서비스와 직접 연결된다. 이러한 소셜 네트워크 서비스 가운데 하이브리드형 서비스는 사용자의 정보를 관리 및 파악하여 사용자가 원하는 제품을 예측하고 추천해주고 있으며, 이를 위해 그래프 마이닝 기술을 적용하고 있다. 하지만 그래프 마이닝 기술은 아직 복잡한 그래프 구조의 데이터에서 정보를 추출하기에 제약사항들이 발생하므로 이에 대하여 많은 연구가 활발히 이루어지고 있다. 이러한 그래프 마이닝 기술을 나아가 더 발전시켜 활용하면 기존의 하이브리드형 서비스에서 사용자의 정보를 파악하여 충성도를 높여줄 뿐 아니라 기업에서의 타켓 마케팅과 원투원 마케팅을 가능하게 해주고 기존 사용자에 대한 교차 판매와 격상판매의 전략들을 도출할 수 있을 것이다.

• Journal of KIISE
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• v.41 no.12
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• pp.1117-1125
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• 2014

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.