• Title/Summary/Keyword: Social Network Analysis Approach

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Theoretical Perspective of Social Network Analysis (네트웍 연구의 사회적 접근과 분석이론)

  • Yoo Sarah
    • Journal of the Korean Society for Library and Information Science
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    • v.20
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    • pp.297-330
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    • 1991
  • The work presents the theoretical background and methodology to network analysis. This approach to information network has been widespread use in many researches of social science. Theoretically based idea of structural analysis of information flow were reviewed and the major perspective and the practical aspects of the analysis were discussed. To show an approach of the methodology to library environment, HISP project which analyzed the network structure of a health information dissemination to a health community was demonstrated.

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Community-based Knowledge Networks: an Australian case study (커뮤니티 기반 지식 네트워크: 호주 사례 연구)

  • Bendle, Lawrence J.
    • Knowledge Management Research
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    • v.12 no.2
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    • pp.69-80
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    • 2011
  • This paper reports on a structural view of a knowledge network comprised of clubs and organisationsexpressly concerned with cultural activities in a regional Australian city. Social network analysis showed an uneven distribution of power, influence, and prominence in the network. The network structure consisted of two modules of vertices clustered around particular categories of creative arts and these modules were linked most frequently by several organisations acting as communication hubs and boundary spanners. The implications of the findings include 'network weaving' for improving the network structure and developing a systemic approach for exploring the structures of social action that form community-based knowledge networks.

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Network Analysis on Communication of Welfare Policy Using Twitter Data

  • Seo, Bojun;Lee, Soochang
    • International Journal of Advanced Culture Technology
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    • v.6 no.2
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    • pp.58-64
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    • 2018
  • This main purpose of the study is to identify social network of communicators sharing information on Bokjiro for publicizing welfare policy. This study employs NodeXL pro to understand networks and their role in the social network. The data for social network analysis was collected from Twitter for a week. The result of the analysis shows that the social network of communicators on Bokjiro does not have many nodes. It also has an independent network with high possibility of information distortion. Little communicators have controlling power in information flow in one way of communication. According to the result, it is not effective for marketing strategy of welfare policy in providing online information through Bokjiro. The study suggests that the government should use the transactional approach to marketing based on agent-oriented activity focusing on the exchange relationship between information providers and demanders in an age of networked intelligence.

Trend Analysis of Data Mining Research Using Topic Network Analysis

  • Kim, Hyon Hee;Rhee, Hey Young
    • 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.

Study on Tag, Trust and Probability Matrix Factorization Based Social Network Recommendation

  • Liu, Zhigang;Zhong, Haidong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.5
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    • pp.2082-2102
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    • 2018
  • In recent years, social network related applications such as WeChat, Facebook, Twitter and so on, have attracted hundreds of millions of people to share their experience, plan or organize, and attend social events with friends. In these operations, plenty of valuable information is accumulated, which makes an innovative approach to explore users' preference and overcome challenges in traditional recommender systems. Based on the study of the existing social network recommendation methods, we find there is an abundant information that can be incorporated into probability matrix factorization (PMF) model to handle challenges such as data sparsity in many recommender systems. Therefore, the research put forward a unified social network recommendation framework that combine tags, trust between users, ratings with PMF. The uniformed method is based on three existing recommendation models (SoRecUser, SoRecItem and SoRec), and the complexity analysis indicates that our approach has good effectiveness and can be applied to large-scale datasets. Furthermore, experimental results on publicly available Last.fm dataset show that our method outperforms the existing state-of-art social network recommendation approaches, measured by MAE and MRSE in different data sparse conditions.

Effects of Network Positions of Organizational Members on Knowledge Sharing (조직구성원의 네트워크 위치가 지식공유에 미치는 영향)

  • Kim, Chang-Sik;Kwhak, Kee-Young
    • Knowledge Management Research
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    • v.16 no.2
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    • pp.67-89
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    • 2015
  • Improving productivity of knowledge workers is an important issue in the 21st century referred as knowledge-based society. The core key word is knowledge sharing among constituents of an organization. The purpose of this study is to combine the social network position factors with attitude and behavior factors, and develop an integrated research model for the knowledge sharing among members of an organization. This study adopted the integrated theoretical framework based on social capital, self-efficacy, transactive memory, and knowledge sharing. Surveys were conducted to 42 organizational members from a department in a leading IT outsourcing company to empirically test the proposed research model. In order to validate the proposed research model, social network analysis tool, UCINET, a structural equation modeling tool, SmartPLS, were utilized. The empirical result showed that, first of all, organizational members' familiarity network position had significant influence on knowledge self-efficacy and transactive memory capability. Second, knowledge self-efficacy and transactive memory capability affected knowledge sharing intention. Third, knowledge sharing intention also had an impact on the job performance. However, organizational members' expertise network position had no significant influence on knowledge self-efficacy and transactive memory capability. This finding reveals the importance of the emotional approach rather than the rational approach in knowledge management. The theoretical and practical implications on the research findings were discussed along with limitations.

Deriving a Strategy for Resolving the Inter-and Intra-generational Digital Divide based on the Continuous Core-periphery Network Model (연속형 중심-주변 네트워크 모형을 통한 세대 간 세대 내 디지털 격차 해소를 위한 전략 도출)

  • Yoo, In Jin;Ha, Sang Jip;Park, Do Hyung
    • The Journal of Information Systems
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    • v.31 no.1
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    • pp.115-146
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    • 2022
  • Purpose The purpose of this study is to find meaningful insights using regression analysis to resolve the digital divide between generations. In the analysis process of this study, social network analysis was applied to approach it with a perspective differentiated from the existing statistical techniques. Design/methodology/approach This study used a social network analysis methodology that transforms and analyzes government-led survey data into relational data. First, the cross-sectional data were converted into relational data, and a continuous core-periphery model and multidimensional scaling method were applied. Afterwards, the relationship between various factors affecting the digital divide and the difference in influence were analyzed by generation. Findings According to the network analysis results, it can be seen that all generations commonly use 'information and news search' and 'living information service'. However, it can be seen that the centrally used services of each generation are clearly different from each other, and the degree of linkage between the services is also clearly different. In addition, it can be seen that the relationship between factors influencing the digital divide by generation is also different.

An Estimated Closeness Centrality Ranking Algorithm and Its Performance Analysis in Large-Scale Workflow-supported Social Networks

  • Kim, Jawon;Ahn, Hyun;Park, Minjae;Kim, Sangguen;Kim, Kwanghoon Pio
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.3
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    • pp.1454-1466
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    • 2016
  • This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.

Analysis of Networks among Design Engineers Using Product Data Objects (제품자료 객체를 이용한 설계자 네트워크 분석)

  • Cha, Chun-Nam;Do, Namchul
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.39 no.3
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    • pp.139-146
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    • 2016
  • This study proposes a methodology to analyse social networks among participating design engineers during product development projects. The proposed methodology enables product development managers or the participating design engineers to make a proper decision on product development considering the performance of participating design engineers. It considers a product development environment where an integrated product data management (PDM) system manages the product development data and associated product development processes consistently in its database, and all the design engineers share the product development data in the PDM database for their activities in the product development project. It provides a novel approach to build social networks among design engineers from an operational product development data in the PDM database without surveys or monitoring participating engineers. It automatically generates social networks among the design engineers from the product data and relationships specified by the participants during the design activities. It allows analysts to gather operational data for their analysis without additional efforts for understanding complex and unstructured product development processes. This study also provides a set of measures to evaluate the social networks. It will show the role and efficiency of each design engineers in the social network. To show the feasibility of the approach, it suggests an architecture of social network analysis (SNA) system and implemented it with a research-purpose PDM system and R, a statistical software system. A product configuration management process with synthetical example data is applied to the SNA system and it shows that the approach enables analysts to evaluate current position of design engineers in their social networks.

Social Network : A Novel Approach to New Customer Recommendations (사회연결망 : 신규고객 추천문제의 새로운 접근법)

  • Park, Jong-Hak;Cho, Yoon-Ho;Kim, Jae-Kyeong
    • Journal of Intelligence and Information Systems
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    • v.15 no.1
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    • pp.123-140
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    • 2009
  • Collaborative filtering recommends products using customers' preferences, so it cannot recommend products to the new customer who has no preference information. This paper proposes a novel approach to new customer recommendations using the social network analysis which is used to search relationships among social entities such as genetics network, traffic network, organization network, etc. The proposed recommendation method identifies customers most likely to be neighbors to the new customer using the centrality theory in social network analysis and recommends products those customers have liked in the past. The procedure of our method is divided into four phases : purchase similarity analysis, social network construction, centrality-based neighborhood formation, and recommendation generation. To evaluate the effectiveness of our approach, we have conducted several experiments using a data set from a department store in Korea. Our method was compared with the best-seller-based method that uses the best-seller list to generate recommendations for the new customer. The experimental results show that our approach significantly outperforms the best-seller-based method as measured by F1-measure.

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